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Wireless Multimedia Sensor Networks Routing

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02.02.2021
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WMSNs development has been fostered by the availability of such inexpensive hardware as the CMOS cameras and microphones. These interconnected devices are capable of digital retrieval of multimedia content such as live streams, videos, still images, and scalar sensor data from the environment. The wireless sensor network supports data traffic to multimedia platforms by the use of data sensors and multimedia sensors as both nodes. The successful functioning requires modern and versatile infrastructure and accompanying applications to be artistically put in place. Protocol routing involves manipulating data streaming, sharing, and determining the path of flow and coverage changes. There are three main routing protocols in this type of network: Swam intelligence, Geographical routing, and the multifaceted method, which gives access to all other protocols and networks. There is a number of factors that may affect the proper operation and efficiency of this network. They include the issues of energy consumption, efficiency in data processing, both within the node and in-network, hardware challenges in the variations of network conditions, quality of service (QoS), ability to achieve software-specific requirements and routing, and path selection for the continuous delivery of multimedia streams.

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Modern society is demonstrating the increasing demand for versatile technology for communication and data transmission. The dynamism of technology and inventions, together with innovation, is increasing at a faster rate as multimedia applications have been accepted as an integral part of daily life of modern society. Wireless multimedia Sensor Network (WMSN) is a multimedia information retrieval and processing platform with a0020huge potential (van Dam & Langendoen, 2003). The development of WMSN has been occasioned by recent advances in micro-sensor and related micromechanical processor systems embedded into each other. These advances have precipitated small, low-cost, distributed sensor devices for possessing, sensing, and signal processing wireless communications (Akyildiz, Su, Sankarasubramaniam & Cayirci, 2002a). The technology has therefore led to the creation of low-dense networks called wireless sensor networks. The WMSN is fitted with digital hardware such as cameras, microphones, and other sensors giving rise to multimedia data content. This technology has resulted in the improvement of the CMOS technology leading to the development of the single chip cameras modules integratable with the sensor nodes (Abazeed, Norsheilla, Zubair, & Ali, 2013). Due to the accompanying hardware, the sensors can capture videos, images, audio, and scalar sensor data and subsequently deliver the resultant files through the sensor networks (Frey, Rhrup, & Stojmenovi 2009).

Protocol routing involves manipulating the flow of data, determining its path of flow and data sharing as well as reporting changes. The routing is dynamic, not static, enabling the network to make adjustments to its conditions from time-to-time (Chong & Kumar, 2003). The infrastructure, therefore, cannot be predetermined and rigid. WMSNs are normally used for intrusion detection, environment and building monitoring, and surveillance application, among other uses (Asgari, Ahmed, & Medjiah, 2015). According to Rodrigues et al. (2011), the WMSNR can also be used for multimedia surveillance sensor networks, health care delivery, and industrial process control.

Despites the increasing popularity of the WMSN platform, Asgari et al. (2015) and colleagues appreciate that there is a handful of challenges that come with this kind of network despite its efficiency. Such challenges that will be discussed in this paper include energy consumption efficiency, data processing – both within the node and in-network, audio-visual bandwidth/rate, and the adaptation to overcome the variation in network conditions (Gowrishankar, Basavaraju, Manjaiah, & Sarkar, 2008). The quality of service (QoS), also referred to as efficiency of service in other literature, aims to meet application-specific requirements and network routing (Akkaya & Younis, 2003a). It also requires proper path selection for the continuous delivery of multimedia streams. Finally, there are the questions of confidentiality and integrity of the data stream in the wake of manipulation and hacking of data content in shared networks (Chong & Kumar, 2003).

Routing Protocols

A routing protocol identifies how routers share information with each other enabling them to decide routes between any two nodes in a shared computer network. The algorithm of routing determines the specific choice of a route. Each router has preceding information only of those networks that are directly attached to it (Stavrakakis et al., 1999). Routing is the process of data transfer from one network to another. Simply, it is the process of tracing paths from a source to every destination. All hosts are directly accessible within a network and do not need to pass through a default gateway (Chaudhari, Rathod, & Budhhadev, 2011). A network involves an intricate and careful direct connection and interconnection of all hosts allowing them to communicate directly with each other (Rahman, Aghaei, Saddik, & Gueaiela, 2008). Routed protocol in this regards is thus referring to data that is being shared across the network. The examples of routed protocols include Internet Protocols, Remote Procedure Call (RPC), Telnet, SMTP, SNMP, DECnet, Novell IPX, Open Standard Institute Networking Protocol, Banyan Vines, AppleTalk, and Xerox Network Systems (XNS) (Rahman et al., 2008). It is, therefore, important to appreciate that there are routing protocols wholly devoted to WMSN. With regards to this kind of routing, the network layers are of great importance to QoS support for the multimedia application (Huang & Fang, 2008). This application determines efficient energy path provision for meeting QoS. It also serves as transition for the interchange of performance factors between the application and MAC layer (Abazeed et al., 2013). Abazeed et al. (2013) discuss various routing protocols dedicated to wireless multimedia sensor network in the journal of sensors surveying and addressing the features of each of them. Some of the routing protocols for the WMSN are discussed below.

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Swarm Intelligence Routing Protocol

Saleem et al. (2011) depict swarm intelligence-based routing system as focusing on collective behavior of systems with many components that coordinate through decentralized control and self-organization. The major concept here is pillared on reverse engineering and the adaptation of collective behaviors of natural systems with a view to designing effective algorithms for distributed optimization. The algorithms are robust, scalable, and adaptive, which are indeed desirable characteristics (Saleem et al., 2011).

The ASAR helps meet different types of services QoS requirements by carefully selecting optimal paths. These services, as Abazeed et al. (2013) illustrate, include R, which requires less bandwidth and high signal-to-noise ratio path. It is event-driven and intolerant to delays and errors (Sharma, Karkhanawala, & Kotecha, 2011). On the other hand, D is a service that may accept the use of jamming and high signal-to-noise ratio paths in its functioning. It operates in query-focused mode and is error intolerant, while it is also tolerant to delay. Lastly, S service’s operation mode of occurrence is a stream query one. It is intolerant to delay and tolerant to error. It hence accepts less traffic and a low signal-to-noise ratio path for the service. Ant colony optimization (ACO) is another component of swarm intelligence routing protocol that derives the ways of managing complex tasks. It is based on the collective and group behavior of ants in nature (Sharma et al., 2011). While moving, they deposit the chemical pheromones on their waymarking the routes. When they meet an intersection on the way, they have a decision to make (Sharma et al., 2011). The SWIRT’s architecture is cluster-based and addresses only the routing scheme between the cluster heads and the sink node (Asgari et al., 2015). Each cluster head generates ants for each type of service (R/D/S) so that it finds different paths. It operates from source to destination, which meets the QoS requirements and is suitable for the traffic type. The operations in this type of routing protocol are controlled by the rule of probability which depends on the pheromone value of the path (Langendoen et al., 2003). Defining detailed particularities of this routing protocol helps determine the move from the current node to the next one, calculate the pheromone value, delay, bandwidth, rate of packet loss, and power use by the system. Sun, Ma, Liu & Zhang (2008) further explain that there are optimum values and a paths table for every service’s cluster head. Moreover, every pheromone table has its one service kind with a given value of pheromone in digital format as well as a specific probability that they will transit to the successive hop.

With all its efficiencies, this protocol also as challenges (Alkhatib & Baicher, 2012). The drawbacks, according to Abazeed et al. (2013), include the hierarchical model problem and congestion. Optimal path set up requires extra calculations that affects the network performance. On power consumption by the protocol route, Rahman et al. (2008) canvass a swarm intelligence-based algorithm which embraces the concept of ant colony optimization. It allows the optimization of the QoS metrics like jitters, delay, energy consumption, and packet survival rate. Moreover, the global state of sensor node is not maintained here and the decision on routing is made based on neighborhood information only. The decision on routing path is further influenced by the effects of both the distance from the hop to the sink and from the current node to the next hop (Sabari & Duraisway, 2009)

Geographical Routing Protocol

Geographical routing protocol involves two schemes that make packets progress towards the destination node. Asgari et al. (2015) and Krap et al. (2000) explain that these greedy progression schemes are based on the distance to the destination node. On the other hand, greedy progression is based on the angular offset in the direction towards the destination node (Deb, Bhatnagar & Nath, 2003). Furthermore, the route between the source and the destination in both schemes is progressively chosen based on node-level forwarding decisions made locally at each hop. Explicitly, geographical routing, according to Asgari et al. (2015), is the process in which each node is sensitive to its geographical coordinates and uses the position of packets destination to perform the routing decision. In this regard, the geographic routing operates with two protocols. Abazeed et al. (2013) agree with Asgari et al. (2015) that TPGF is the two-phase geographic greedy forwarding algorithm for WMSN.

The presentation by Abazeed et al. (2013) is in accordance with the research by Almazroi & Ngadi (2014). Both authors explain that the first phase explores possible routing paths, while the second phase optimizes found routing paths based on the smallest number of hops. The type of protocol assumes that each node is aware of its location and its one hop neighbor node location. Each node has three states: active and available, active but unavailable, and the dead state. Moreover, the links have two states named available and unavailable (Al Turki & Mehmood, 2008). The routing paths should be achieved through active and available nodes and available links in order to evade the holes and then optimize the found routing paths based on the smallest number of hops. The two-phased system is therefore suited by path optimization and geographic forwarding. The paths for routing are traced by geographic forwarding set having bypassing holes using set back and mark methods. The node will mark itself as a block node and eliminate the path circle in the absence of the next node or the previous one.

Therefore, the greed forward method has been presented above. Label-based optimization is used to add other services to the forwarding phase. The label-based method will have every chosen node equipped with labels together with path number and a digressive node number (Alwan & Agarwal, 2009). Another type of the geographic routing protocol is the GEAMS, which is a geographical multipath protocol designed to prolong the survival or the longevity of the network. This type is an enhancement of the GPRS protocol with the feature of load balance added to increase speed, reduce queue size and prolong the lifetime of the network (Karp & Kung, 2000). Additionally, it helps with the remaining energy, the number of hops, the distance between the node and its neighbors and the chronology of the packet belonging to the same stream.

In order to check on the power usage of the system, the routing system uses two methods. The first method is used when there is a neighbor closer to the destination than the current node (Pereira et al., 2007). Another one is useful when there is a congestion and the next hop cannot be found towards the destination by the forwarding node. Each node in this protocol only gathers the topological information about its immediate ecological surroundings, which is the main benefit of this kind of protocol. Therefore, its forwarding component depends on the local knowledge of the selection of the closest next hop node to the destination (Asgari et al., 2015). Asgari et al, (2015) propose the use of ant colony algorithm and game theory principle to address the QoS challenge in wireless multimedia sensor network. The proposal mentioned by Asgari et al. (2015) and Olariu & Xu (2008) involves three integral elements: payoffs, strategies, and players. As shown above, the sensor node uses immediate surrounding information to build a routing path, which depends on the command result and the residual energy.

Routing Protocols Addressing Different Types of Algorithm

The requisite protocol uses metadata to construct a multipath routing to meet QoS particulars (Yao et al., 2009). As proposed by Yao et al. (2009), the protocol uses the cost function and advanced Dijkstra algorithm. The model has an effect of reducing neighbor nodes by discriminating those with insufficient reserved energy and uses at least two factors when making decisions with regards to the routing path (Kumar et al., 2011). Cost, in this case, is arrived at based on the multipath delay and energy consumption features. Afterward, the path with high optimization is selected. End-to-end delay is calculated based on the processing and the queuing delays of the relay nodes, together with the distance between the nodes (Radi, 2012). Just like the four discussed routing protocols for this kind of network, introducing a confidential queue model at each node to categorize real-time data and non-real time data can be used to reduce queuing delay (Misra, Reisslein, & Guoliang, 2008). Metadata is utilized for the description of the packets.

This protocol type is superior compared to SAR protocol with respect to delay as well as the power being utilized. In order to minimize energy consumption, the metadata is disengaged from use in this protocol, as it causes overhead and increased energy consumption. In order to put a cap on power use, control data traffic congestion in the system and boost the reliability of the WMSN system, a routing protocol that does load balancing and uses multipath is needed (Guannan et al., 2011). This protocol is flat and event-driven and requires no wide area topology with the sensor node only aware of its neighbor nodes hence reducing the overhead. Besides the multipath as its salient feature, it has three full disjointed paths built from source node to sink named as back up, primary, and alternate paths (Guannan et al., 2011). The degree of delay is represented by the disjointed paths with the primary path being the delay one, followed by an alternate, and, finally, the backup paths.

The backup path is used in case of a default failure of the primary and alternate paths (Abazeed et al., 2013). The congestion control mechanism is aided by the fact that the primary path is faster and has more time. It is also used by two paths as a relay node as it is designed for the major node for monitoring the queue of this node if the receiver queue reaches its capacity. The protocol, according to Abazeed et al. (2013), is low in redundancy meaning a strong influence on reliability.

Issues Affecting Multimedia Routing in Wireless Sensor Network

WMSNs are wireless sensor networks that support data traffic in multimedia platforms by the use of data sensors and multimedia sensors as both nodes. The requirement of the WMSNs is much different and specific as compared to other types of wireless connectivity. Therefore, it requires specific protocol support [to be designed for each layer (Poonia, Singh, & Kumar, 2011).

The statement by Poonia et al. (2011) and the related group of scholars definitely means that this kind of network is prone to being affected by various factors. This characteristic makes it operate within a dimension of such challenges as those related to sensor nodes. These challenges may be hardware-based in terms of their capacity defining the limits of any application or optimization proposal in wireless sensor network (WSN) (Masouri, Sardouk, Merghem-Boulahia, Gaiti, & Snoussi, 2010). Hardware related challenges tend to be more technical and mechanical. They may be in the form of limited memory, CPU technicalities radio communication, or power constraints (Masouri et al., 2010).

Another challenge concerns energy consumption. Abazeed et al. (2013) argue that due to the high volume of traffic requiring high transmission rates and processing by multimedia networks, there is high energy consumption by the WSN (Girod, Aaron, Rane, & Rebollo-Monedero, 2005). The system takes toll on power consumption on a network having a battery constrained sensor due to the large volume of data, high transmission rate, and extensive processing in WMSN (Rahman et al., 2008). It, however, can be capped by software protocol routing manipulation, as it has been shown above

Another issue is related to efficiency (Alghamdi, Xie, & Qin, 2005). QoS necessary to be followed in differentiated data processing with regards to types and characteristics of data and traffic that come from various applications manipulated in the network. Such applications include environment monitoring, data collection and disaster control together with energy consumption components in order to deliver on wireless multimedia sensor network. The quality of service is the biggest issue in WMSN determining network performance and its streaming task (Masouri et al., 2010). Moreover, it determines the capacity of WSN to handle the multimedia speed and traffic. QoS is specified at the beginning of any network design (Duraiswamy et al., 2009). This component affects the performance greatly because it even specifies the layering of the system. Masouri et al. (2010) continue to illustrate this fact by explaining that when viewing the system efficiency, it is important to appreciate that QoS is responsible for the network layer that performs a number of important functions. It provides efficient and stable routes, which guarantee the end-to-end QoS specifications and intermediate the performance parameters of application layers and MAC.

Masouri et al. (2010) recommend that special modification is, therefore, required in terms of hardware and high level algorithms for delivering QoS specific requirement applications. When QoS is compromised, such important WSN performance parameters as reliability, distortion, network lifetime, and energy consumption may be deteriorated and the algorithms might reach multiple domains.

Routing presents another major challenge to WSN multimedia streaming because of longer periods of logged sessions, which require extremely efficient routing algorithms in order to support streaming multimedia application and large-sized data packets (Masouri et al., 2010). Masouri et al. (2010) and other researchers note that there are three main reasons that make routing a monumental issue in the WSN spectrum. The first issue is the fact that large-sized data packets in WMSNs require multiple paths for efficient transmission. The second one is the need for hole-bypassing. Finally, while the third reason is the fact that shortest path requires the minimization of the end-to-end delay.

Alternative Remedies. Because of the above challenges, as Eisenberg, Luna, Pappas, Berry and Katsaggelos explain, there is a need to make arrangements so that routing algorithm supplements these requirements with little or no complexity while simultaneously meeting the QoS specifications (2002). The first applicable approach is the end-to-end throughput, since there must be a new protocol definition to improve the network promptly. Transport layer functioning is affected by the medium contention in wireless networks. Hence, it affects the efficiency of the multi-hop wireless networks (Eisenberg et al., 2002). Medium contention affects data flow to both ends. Masouri et al. (2010) recommend two protocols adapted to the variable bandwidths. These bandwidths are ASAR (Adaptive selective repeat), which configures transmission timeouts dynamically, and RLC (Radio link control) protocol, which does not bank on retransmission timeouts.

Another factor that causes challenges to be considered is resource allocation. It affects the network’s inherent capacity to process the commanded task efficiently. This process is, in turn, affected by the system’s processing capability, achievable data rate and physical as well as hardware resources such as memory, storage devices, and power stability. An example is the case when the system is continuously logged in session or multiple sessions bringing about WSN inflexibility (Rebollo-Monedero et al., 2005). There is also the need to consider bandwidth demand, because a command or operation with higher bandwidth demand than those required by data sensors streaming in WMSN requires higher data rates and lower power consumption-transmission techniques (Shu, Zhang, Yang, Wang, & Hauswirth, 2008).

Another challenging consideration concerns variable channel capacity. According to Shu et al. (2008), the level of interference detected at the receiver determines the capacity of WSN in a multi-hop wireless network. This interference is usually caused by such factors as power control, routing, and even data rate policies being handled by network devices in a distributive manner (Shu & Chen, 2010).

Coverage is another issue that affects the network. WMSN-related sensors possess large radiuses with wide mass coverage as well as high sensitivity to acquisition direction (Akyildiz, Melodia & Chowdhury, 2006). The coverage design of a data sensor network may fall short of being sufficient in light of the abovementioned fact. Therefore, a great challenge is posed for the multimedia sensor network. A solution will be a new model that can accommodate larger multimedia coverage (Capone & Stavrakakis, 1999). There is also a need to consider source encoding and compression. Less complex techniques are required here. They are also less tolerant and require less power while due to giving lower output bandwidth (Masouri et al., 2010). These techniques use either intra-frame compression reducing the idleness within a frame or inter-frame compression like motion evaluation or extrapolative coding using the idleness within frames. However, Singh, Singh & Singh (2010) contend that predictive coding techniques require multifaceted encoders, dominant processing algorithms, and high power requirement. Thus, low-cost multimedia sensor networks become inappropriate (Singh et al., 2010).

The use of encoding and compression techniques is preferred for still images, as it allows to visualize video as sequence of images avoiding motion estimation and recompense. An example is the distributed source coding that utilizes source decoder statistics to design a complex decoder thus keeping the encoder simple (Rahman et al., 2008).

How Multimedia Constraints Affect Routing Performance

Multimedia constrained by the issues discussed in the above section will not function well. Some of the ways the routing performance is affected are explained below and have been studied by Sivagami et al. (2014). The interaction of the WSN routing systems tends to be far from the conventional hence it can have some traits different from those in the environment (Rebollo-Monedero et al., 2005). The effects on the routing systems are, therefore, pronounced. They include severe resource constraint, important impact of resource constraints such as power, memory, bandwidth, routing system’s processing and transferring ability (Al-Karak & Kamal, 2004).

Energy is the most fundamental resource, and the routing protocol comes to a sudden stop and breaks down in case of its deficiency. Primary energy cannot be recharged or replaced. Sivagami et al. (2014) have concluded that the constraint inflicts unnecessary tension and pressure on the network causing it to shutter (Perillo & Heinzelman, 2003). It may also cause unbalanced traffic. Traffic majorly flows from a large number of sensor nodes into a small subset of sinks (Akkaya & Younis, 2003b). A constrained routing protocol will affect the flow of data, speed, and the amount transmitted. Moreover, it could affect the operation making it difficult to transmit a file through the network (Akyildiz et al., 2009).

Another issue that may arise is data redundancy. It normally comes with latency and complicates the QoS design in WSNs. Finally, one must deal with network dynamics (Aaron, Rane, Zhang & Girod, 2003). It is usually brought about by node failure, wireless link failure, node mobility, or node state transition, which comes from the usage of power management or efficient energy schemes. High network dynamism greatly increases the QoS support complexity (Vidhyapriya & Vanathi, 2007).

The Uses of Wireless Multimedia Sensor Networks

Creating a WMSN involves equipping a single sensor device with audio and visual information collection modules. Besides digital data retrieval, WMSN is also used for storing, real-time processing, fusing, and correlating multimedia data originated from heterogeneous sources (Vanathi et al., 2007). The network, together with the right protocol, will help enhance existing sensor network applications such as automobile monitoring, tracking functions, home automation and immediate environment monitoring (Wang & Balasingham, 2008). Besides, as Wang & Balasingham (2008) illustrate, wireless multimedia network is also able to run several new applications including multimedia surveillance sensor network where video and audio sensors are used to enhance and complement existing digital security surveillance systems against crime and terrorist activities (Ye, Heidemann, & Estrin, 2004). Such applications include CCTV cameras with a complex scale of networks of video recorders used by law enforcement officers and agencies to monitor city areas, public events, borders, and major installations (Akyildiz, Su, Sankarasubramaniam & Cayirci, 2002b).

Traffic Congestion Avoidance Systems is also used for traffic monitoring and controls in bigger cities and highways. Devices within this network are used to help traffic advisories and drivers avoid traffic congestion (Akyildiz et al., 2006). Another application could be automated parking assistance. Advanced Health Care Delivery is a modern application that incorporates telemedicine sensor networks along with 3G multimedia networks that provide fast digital medical services (Younis et al., 2003). Patients may use such self-assistance applications as medical sensors to monitor parameters body temperature, blood pressure, pulse oximetry, breathing activity, and other body parameters. This device will help provide real-time and essential support to disadvantaged and disabled parts of the society (Younis et al., 2005).

Finally, there is the Industrial Process Control network, which is a machine vision system that can be integrated with the wireless multimedia sensor network (Ahmedy, Ngadi, Omar, & Chaudhry, 2011). It simplifies and makes industrial operation systems for visual inspections and automated sections more flexible. It also enables actions that require extremely high speed, magnifications, and continuous operations (Akan, Akyildiz & Sankarasubramaniam, 2005). Multimedia aspects and contents of relevance that could be exploited for this application include imaging, temperatures, or pressure amongst others (Aaron et al., 2003).

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Conclusion

There has been a total shift in communication and data handling across the whole world. This industry has influenced virtually every aspect of human life. Hence, the demand for such innovative products has increased as well. The increasing demand for cutting edge technologies providing higher throughput, reliable data transmission, and stable infrastructure has caused more pressure on the traditional network infrastructures and technology landscape. The technological landscapes have previously been either infrastructure-based or infrastructure-less. Moreover, the situation has been worsened by the alleviated demand for live streaming by multimedia applications that are grasping the major portion of bandwidth in all communication standards. This situation can be vindicated by the invention of a 4G network currently under tests of major communication and data manipulation houses across the globe.

Although the current 3G cellular networks and Wireless LANs have already met these demands, their infrastructure-based functionality limits their role during network jamming or breakage, implying the demand for a more robust infrastructure. The solutions to these issues require much capital and time investment that limits its growth in the field of ever-demanding wireless networks. WMSN comprises a similar sensor network as in WSN, except for the fact that WMSN deploys sensors capable of audio/video sensing and processing capabilities along with data sensor nodes. Many approaches and protocols have been found to be costly and unsuitable for WMSN, and have since been modified and proposed for traditionally distributed systems. These approaches are under tight coordination requiring a protocol based extensively on message exchange and wireless communication. This solution will, though, consume a lot of power. All these standards have been adopted globally due to the recommendations of the IEEE considered as the de facto standard for communication and technology in WSN. It is, therefore, safer to conclude that swarm intelligence, as well as all the discussed routing protocols, have many components that coordinate decentralized control and self-organization. Finally, the routing protocol has features impinging a number of constraints on the wireless multimedia.

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