Review of energy efficient block-matching motion estimation algorithms for wireless video sensor networks

Block matching motion estimation is a significant issue in image and video super resolution reconstruction. In order to decrease the number of search locations, the amount of computations, and enhance the prediction accuracy, an improved block matching motion estimation algorithm based on adaptive rood pattern search (ARPS) is proposed. Firstly, we use spatial correlation characteristic to predict motion vector (MV) of the current macro-block in the algorithm. Secondly, we consider to eliminate the influence of neighborhood macro-blocks having low relevance to the current macro-block in the prediction process. Finally, we utilize the weighted average MV of two macro-blocks with the smaller matching error in the neighborhood macro-blocks to calculate the predictive MV of current macro-block, instead of using the fixed single macro-block forecast pattern in ARPS. Experimental results show that our algorithm provides the better performance in terms of the computational complexity and the prediction precision in comparison with APRS and other block matching algorithms.

Exclusive benefits gained by using the image and video sensors in Wireless Sensor Networks (WSNs), make the Wireless Video Sensor Networks (WVSNs), design and development of their structures, a trending topic. The main challenges of WSNs which are more critical in WVSNs are throughput and end-to-end delay. The radio transceivers recently used in WSN motes can work on different channels with very low switching time. Therefore, many multichannel protocols are proposed to improve the efficiency and throughput and decrease the packet drop rate of the network. Proposed algorithms rarely provide a particular solution to the end-to-end problem. Such delays are necessary parameters in real-time applications. In this paper a real-time multichannel protocol called RTMCH is suggested to address the end-to-end delay of a stream in WVSN. A flow-based channel assignment strategy is used for this purpose. Orthogonal channels are assigned to each flows based on many-to-one data-flow to minimize the contention and collision between different flows. Transmission power is a parameter that can be controlled to achieve desired link quality and to control link delay. Channel assignment problem aligns to a constrained optimization problem to make the specified end-to-end delay of each flow. A channel assignment and real-time packet forwarding scheme are then presented. Simulation results based on realistic channel and radio model shows that the RTMCH can efficiently use multiple channels and transmission power to meet specified end-to-end delay. The results also show better performance for RTMCH over a recent real-time protocol and basic multichannel schemes.

In recent years, a new wave of networks labelled Wireless Sensor Networks (WSNs) has attracted a lot of attention from researchers in both academic and industrial communities. A WSN consists of a collection of sensor nodes and a base station connected through wireless channels, and can be used for many applications such as military application, building distributed systems, physical environment monitoring, and security surveillance among others. A big advantage of sensor networks is represented by ease of deployment, reducing installation cost, possibility to distribute the tiny sensors over a wide region, and larger fault tolerance (V. Loscri et al., 2005). However, despite the infinite scopes of wireless sensor networks applications, they are limited by the node battery lifetime. Such constraints combined with a typical deployment of large number of sensor nodes have posed many challenges to the design and management of sensor networks and necessitate energyawareness at all layers of the networking protocol stack (Q. Xue & A. Ganz, 2004). Therefore, energy efficient algorithms have been one of the most challenging issues for WSNs. Sensor nodes can be in one of four states, namely transmit, receive, idle and sleep. The largest part of a node’s energy is consumed while transmitting and receiving. Minimizing the number of communications by eliminating or aggregating redundant sensed data saves much amount of energy (L. B. Ruiz et al., 2003). Among these clustering sensor networks are a very attractive approach because clustering allows for scalability, data aggregation, and energy efficiency. In a clustering network, nodes are grouped into clusters and there are special nodes called cluster head. They are responsible for an efficient way to lower energy consumption within a cluster by performing data aggregation. In a heterogeneous sensor network, two or more different types of nodes with different battery energy and functionality are used. On the other hand, in homogeneous networks all the sensor nodes are identical in terms of battery energy and hardware complexity. As a result, network performance decreases since the cluster head nodes goes down before other nodes do. Thus dynamic, energy efficient and adaptive cluster head selection algorithm is very important. Sensor networks can be divided in two classes as event-driven and continuous dissemination networks according to the periodicity of communication (L. B. Ruiz et al., 9

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

Wireless sensor networks (WSN) have become the rising stars of technology. Every object in this world tends to be sensorly developped, monitored and controlled. Monitoring an area of interest for security reasons for millitary applications, catastrophic natural events, ... gives each captured frame a huge importance to be able to achieve this surveillance system. Thus, Wireless Video Sensor Networks (WVSN) represents the leading technology to implement this kind of surveillance systems. A WVSN consists of three different layers: The video-sensor node, the coordinator and the sink. A video sureveillance system can have hundreds or thousands of video-sensor nodes. Hence, several challenges exist in such a densly deployed system. The leading challenge is for sure the energy consumption problem for capturing, processing and transmitting several images on the network, but it is not the only challenge. Data transmitted on the network from several sensor nodes to a coordinator must be securized. Thus, the emergence of the security challenge in WVSN. In this paper, on the sensor-node level, for data reduction, a new algorithm has been proposed. This algorithm adapts the frame rate and reduce the number of images sent from the sensor node to the coordinator. This algorithm is compared to our most recent algorithm in Salim et al. (24). For the security challenge, the one-round algorithm from Noura et al. (20) is adapted to our approach and scenario. This approach is validated by experimentations using Cpp for OpenCV on Raspberry Pi 3 and by comparing it to other previous, existing approaches.

A series of challenges exist in the process of sensor network research which can not be solved by one sensor node. Cooperative communication, based on the idea of spatial diversity, which makes different users or nodes in wireless network share their antennas or other network resources, is a promising technique toward improving performance of wireless sensor network significantly. This article adopts cooperation between nodes and opportunistic collaboration transmission strategy is proposed. Energy efficient power allocation algorithm in wireless sensor network is put forward to optimize performance of opportunistic cooperative transmission. Through the simulation, regardless of statistical feature of channel state of opportunistic node, opportunistic cooperation can effectively improve the performance of the system. Using energy efficient power allocation algorithm can effectively reduce energy consumption of the network and improve the network performance

Block-matching algorithm is the most common technique applied in block-based motion estimation technique. There are several block-matching algorithm based on block-based motion estimation techniques have been developed. Full search (FS), three step search (TSS), new three step search (NTSS), diamond search (DS) and hexagon based search (HS) are the most well known block-matching algorithm. These techniques are applied to video sequences to remove the temporal redundancy for compression purposes and to gauge the motion vector estimation. In addition, the mentioned block-matching algorithms are the baseline techniques that have been used to further develop all the enhanced or improved algorithms. In order to develop the proposed methods, the baseline techniques are studied to develop the proposed algorithms. This chapter proposes modelling of fast unrestricted hexagon search (FUHS16) and unrestricted hexagon-diamond search (UHDS16) algorithms for motion vector estimation, which is based on the theory and application of block-based motion estimation. Both of these algorithms are designed using 16 × 16 block size. In particular, the motion vector estimation, quality performance, computational complexity, and elapsed processing time are emphasised. These parameters have been used to measure the experimental results. It is the aim of this study that this work provides a common framework with which to evaluate and understand block-based matching motion estimation performance. On the theoretical side, four fundamental issues are explored: (1) division of frame, (2) basic block-based matching, (3) motion vector estimation, and (4) block-matching algorithm development. Various existing block-matching motion estimation algorithms have been analysed to develop the fundamental research. Based on the theoretical and fundamental research analysis the FUHS16 and UHDS16 algorithms using 16 × 16 block-based motion estimation formulations were developed. To improve the UHDS16 algorithm, 8 × 8 block-matching technique has been tested. The 8 × 8 block-matching technique is known as UHDS8. The results show positive improvements. From an application perspective, the UHDS8 algorithm efficiently captured the motion vectors in many video sequences. For example, in video compression, the use of motion vectors on individual macro-blocks optimized the motion vector information. The UHDS8 algorithm also offers improvement in terms of image quality performance, computational complexity and elapsed processing time. Thus, this chapter offers contributions in certain areas such as reducing the mechanism of computational complexity in estimating the motion from the video sequences. In particular, the FUHS16, UHDS16 and UHDS8 algorithms were developed to estimate the motion vectors field in the video sequences. Theoretical analysis block-based matching criteria are adapted to FUHS16, UHDS16 and UHDS8 algorithms, which are based on search points technique. Basically, the proposed of FUHS16, UHDS16 and UHDS8 algorithm produces the best motion vector estimation finding based on the block-based matching criteria. Besides that, the UHDS8 algorithm also improves the image quality performances and the search points in terms of the computational complexity. Overall, the study shows that the UHDS8 algorithm produces better results compared to the FUHS16 and UHDS16 algorithm.

Recent technological advances in microelectronics and nano-systems technologies have made it feasible to equip wireless sensor nodes with small low-cost cameras to capture and transmit video. Wireless video sensor networks are gaining popularity due to numerous potential applications such as video surveillance, environmental and habitat monitoring, and so on. However, due to the limited battery available in wireless video sensor nodes, provisioning of QoS in such a network is a challenging task. We provide a survey on the major issues related to QoS provisioning in wireless video sensor networks and possible solution approaches. A dynamic power management framework is proposed for a wireless video sensor node to improve energy saving performance so that the lifetime of the sensor node can be increased. This framework considers the video traffic arrival process in the sensor node, the sleep and wakeup processes in the camera and wireless transceiver electronics, the queue status, and the wireless channel condition. Performance analysis results show that the proposed mechanism can achieve considerable energy saving in a sensor node while providing a target level of QoS performance.

Energy efficiency is critical in wireless sensor networks (WSN) for system reliability and deployment cost. The power consumption of the communication in multi-hop WSN is primarily decided by three factors: routing distance, signal interference, and computation cost of routing. Several routing algorithms designed for energy efficiency or interference avoidance had been proposed. However, they are either too complex to be useful in practices or specialized for certain WSN architectures. In this paper, we propose two energy efficient geographic routing algorithms (EEGRA) for wireless sensor networks, which are based on existing geographic routing algorithms and take all three factors into account. The first algorithm combines the interference into the routing cost function, and uses it in the routing decision. The second algorithm transforms the problem into a constrained optimization problem, and solves it by searching the optimal discretized interference level. We integrate four geographic routing algorithms: GOAFR+, Face Routing, GPSR, and RandHT, to both EEGRA algorithms and compare them with three other routing methods in terms of power consumption and computation cost for the grid and irregular sensor topologies. The results of our experiments show both algorithms conserve sensor's routing energy 30% ~ 50% comparing to general geographic routing algorithms. In addition, the time complexity of EEGRA algorithms is similar to the geographic greedy routing methods, which is much faster than the optimal SINR-based algorithm.

Most multi-object detection and tracking techniques suffer from the well-known "multi-object occlusion" problem. The abundant nodes of wireless video sensor networks (WVSNs) can be utilized to solve the problem, and the video nodes in WVSN have limited calculation capability and energy. In order to achieve effective multi-object tracking using WVSN, the main contributions of our proposed method are that: (1) the limits of field of view (FOV) of every video nodes are utilized to establish the consistent labeling of the objects in different views. (2) Mobile Agent is employed to communicate among network nodes, so the objects are assigned correct labels after multi-object occlusion. The performance of the approach has been demonstrated on real-world and the experimental results show that the proposed method is effective for resolving multi-object occlusions and meets the requirement of WVSN.

The availability of low cost networked wireless devices and video cameras is enabling wireless video sensor networks (WVSNs), which can be used in scenarios such as healthcare, agriculture, smart cities, intelligent transportation systems, and surveillance. These scenarios typically require that each node sends a video stream to a server located in the cloud. The IEEE 802.11 is considered a suitable technology for transmitting video wirelessly, as it supports high data rates. However, when using a multi-hop topology to extend the IEEE 802.11 coverage, the IEEE 802.11-based WVSNs suffer from three problems: low network capacity, throughput unfairness, and energy inefficiency. To overcome these problems, we propose a holistic solution, named Green wiReless vidEo sENsor NEtworks uSing out-of-band Signalling (GREENNESS). GREENNESS combines a node polling mechanism with the use of out-of-band signaling over a low power radio to signal when a video sensor should switch ON and OFF its IEEE 802.11 interface, thus saving energy. The results obtained for random network topologies show that GREENNESS can achieve energy savings up to 92%, and improve network capacity and throughput fairness when compared to state of the art CSMA/CA-based WVSN solutions.

Clustering is an efficient technique to improve scalability and life time of wireless sensor networks (WSNs). However, energy efficiency of the sensor nodes and load balancing of the cluster heads (CHs) are the two most important issues that must be addressed in designing clustering algorithm for WSNs. In this paper, we first present a clustering algorithm called energy efficient load-balanced clustering algorithm (EELBCA) that addresses energy efficiency of the sensor nodes and load balancing of the CHs in terms of cardinality of the clusters. EELBCA is a min-heap-based clustering algorithm which is shown to run in O(n log m) time for n sensor nodes and m CHs. We next propose a parameter-based clustering algorithm (PBCA) that considers the energy efficiency of the sensor nodes and also the load balancing of the CHs in terms of overall energy consumption by incorporating communication load of the CHs with respect to the base station. The algorithm requires O(n log n) time. The experimental results show the efficiency of the proposed algorithms in terms of execution time, load balancing, energy consumption, number of live sensor nodes and the number of live CHs.

In wireless sensor networks, energy efficiency is an important standard for evaluating the effectiveness of the time synchronization protocols. Hence, energy should be conserved as much as possible provided that algorithm can achieve a certain accuracy. In this paper, we propose an energy efficient time synchronization algorithm in which each layer of the network can be synchronized merely by receiving broadcast messages rather than relying on two-way message exchange. Simulation results show that our improved strategy reduced the energy consumption and prolonged the lifetime of network compared with TPSN.

A wireless ad hoc network is composed of several tiny and inexpensive device such as wireless sensor networks (WSNs) which have limited energy. In this network energy, efficiency is one of the most crucial requirements. Data transmitting in minimum power level is one way of maximizing energy efficiency. Thus, transmission power level of nodes should be managed in a smart way to improve energy efficiency. Topology control is one of the main algorithms used in a wireless network to decrease transmission power level while preserving network connectivity. Topology control could improve energy efficiency by reasonably tuning the transmission power level while preserving network connectivity in order to increase network capacity and lifetime. In pursuit of energy efficiency and connectivity, nodes can be selfish and are conflicting with each other. Therefore to overcome the conflict, game theory is used to construct energy efficient topology, as well as minimizing energy consumption. In this paper, the main goal and most recent energy efficient topology control algorithms in WSNs and ad hoc network are classified and studied according to their specific goals.

Distributed video coding (DVC) is an emerging video coding paradigm designed to allow low-complexity compression by resource-constrained video sensor nodes in wireless video sensor networks (WVSN). Several DVC architectures have been proposed in literature but there are hardly any security mechanisms designed to validate the integrity and authenticity of the decoded video. In this paper, we propose a novel, low-complexity, blind, and imperceptible video watermarking scheme based on DVC for WVSNs. The watermark information is embedded into key frames under selected GOP (Group of Pictures) configurations. Considering the information-intensive yet resource-constrained WVSN environment, the proposed scheme aims to limit the impact of watermark embedding complexity by hiding the secret information using non-zero quantized AC coefficients. We implemented the proposed watermarking scheme for a DVC based codec and evaluated its processing complexity (encoding/decoding). We also evaluated the average normalized correlation between the embedded and extracted watermarks for different video sequences by varying the redundancy in embedding.