Power Efficient Clustering for Wireless Multimedia Sensor Network

Abstract

The availability of inexpensive hardware such as CMOS cameras and microphones has fostered the development of wireless multimedia sensor networks (WMSNs). In WMSNs, wirelessly interconnected devices enable ubiquitously retrieving multimedia contents such as video and audio streams, and still images along with scalar data from surroundings for wide range of applications are constrained by processing, memory, and power resources. Image compression via low-complexity and resource efficient transforms has been addressed by several researchers to prolong network lifetime where energy conservation is achieved through sharing computational load among sensor nodes and by adjusting the transmission ranges of camera nodes. However, those schemes are not adaptive to the presence and changes of energy level of computational sensor nodes and to the amount of computational load. We propose a resource and energy efficient distributed image compression algorithm that dynamically configures according to the energy levels and the forwarding strategy that is based on the entropy of the image. The simulation results show that our adaptive distributed image compression scheme significantly prolongs the network lifetime and improves the network utilization efficiency, while maintaining adequate image quality.

Distributed Image Compression Architecture over Wireless Multimedia Sensor Networks

“…FLS consists of four key components: fuzzier, inference engine, fuzzy rules, and defuzzifier, as shown in Figure 3 [32]. if SN receives the broadcasting message then (4) if Message is Level-Msg then (5) if routing level of node != 0 then (6) if routing level of node > routing level within received message then (7) Update routing level of SN (8) if Node is CH then (9) routing level ← routing level + 1 (10) Broadcast new Level-Msg (ID, routing level) (11) end if (12) end if (13) Else (14) Update routing level of SN with routing level within received message (15) end if (16) else if Message is Level-Request-Msg then (17) if routing level of node != 0 then (18) Send The fuzzifier gets crisp inputs and converts them to a fuzzy set, which is represented by a linguistic term, such as "near," "medium," or "far," using a membership function. This process is known as fuzzification.…”

“…ICH encodes HH, HL, and LH (8) ICH sends the LL to nearest member node in the image compression cluster (9) Else (10) ICH performs all compression tasks (11) ICH sends the compressed data to the ICH's communication CH (12) end if (13) end if (14) if Compression tasks are finished then (15) ICH combines all compressed data and sends them to the ICH's communication CH (16) end if (17) else if Node is member of the image compression cluster then (18) if Node receives LL from another SN in the image compression cluster then (19) Find the next-nearest SN in the image compression cluster (20) if Has the next-nearest node in image compression cluster then (21) Node applies one level of DWT on LL of the previous level (22) Node encodes HH, HL, and LH and sends the compressed data to the ICH (23) Node sends the LL of this level to the nearest member node in the image compression cluster (24) Else (25) Node performs the remaining compression tasks (26) Node sends the compressed data to the ICH (27) end If (28) end If (29) (10) end for (11) Next CH ← Select the CH that has the highest fuzzy cost (12) Source CH sends the compressed image to Next CHs. (13) Else (14) Source CH sends the compressed image directly to the base station.…”

“…(13) Else (14) Source CH sends the compressed image directly to the base station. (15) end if (16) if Node receives Relay-Msg then (17) CH replies to the source CH with Relay-Reply-Msg (ID, routing level, energy). (18) end if (19) end if (20) end while Algorithm 4: Relay node selection.…”

“…A few dead SNs can cause the failure of the entire network, especially SNs that break or divide the network into an unbalanced structure. Accordingly, in the design of a WMSN routing protocol, distributed image compression must be considered [17]. However, designing a distributed scheme that overcomes the limitations of the SN to prolong the network lifetime is challenging since there is always a trade-off between compression quality and energy (such as computing and transmission power).…”

An Effective and Robust Decentralized Target Tracking Scheme in Wireless Camera Sensor Networks

“…Similar to [26,27], we suppose a joule (J) to be the energy expenditure for a camera node acquiring a frame and each frame processing produce, on average, $b_t$ bits for transmission, p is the average energy in joule (J) required for processing and producing 1 bit of information to be transmitted, and t and r is the average energy in joule (J) consuming in transmitting and receiving 1 bit, respectively.

Active state.

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