A collaborative node management scheme for energy-efficient monitoring in wireless multimedia sensor networks

Clustering in sensor networks provides energy conservation, network scalability, topology stability, reducing overhead and also allows data aggregation and cooperation in data sensing and processing. Wireless Multimedia Sensor Networks are characterized for directional sensing, the Field of View (FoV), in contrast to scalar sensors in which the sensing area usually is more uniform. In this paper, we first group multimedia sensor nodes in clusters with a novel cluster formation approach that associates nodes based on their common sensing area. The proposed cluster formation algorithm, called Multi-Cluster Membership (MCM), establishes clusters with nodes that their FoVs overlap at least in a minimum threshold area. The name of Multi-Cluster Membership comes from the fact that a node may belong to multiple clusters, if its FoV intersects more than one cluster-head and satisfies the threshold area. Comparing with Single-Cluster Membership (SCM) schemes, in which each node belongs to exactly one cluster, because of the capability of coordination between intersected clusters, MCM is more efficient in terms of energy conservation in sensing and processing subsystems at the cost of adding complexity in the node/cluster coordination. The main imposed difficulty by MCM, is the coordination of nodes and clusters for collaborative monitoring; SCMs usually assign tasks in a round-robin manner. Then, as second contribution, we define a node selection and scheduling algorithm for monitoring the environment that introduces intra and inter-cluster coordination and collaboration, showing how the network lifetime is prolonged with high lifetime prolongation factors particularly in dense deployments.     

SoC Issues for RF Smart Dust

Wireless sensor nodes are autonomous devices incorporating sensing, power, computation, and communication into one system. Applications for large scale networks of these nodes are presented in the context of their impact on the hardware design. The demand for low unit cost and multiyear lifetimes, combined with progress in CMOS and MEMS processing, are driving development of SoC solutions for sensor nodes at the cubic centimeter scale with a minimum number of off-chip components. Here, the feasibility of a complete, cubic millimeter scale, single-chip sensor node is explored by examining practical limits on process integration and energetic cost of short-range RF communication. Autonomous cubic millimeter nodes appear within reach, but process complexity and substantial sacrifices in performance involved with a true single-chip solution establish a tradeoff between integration and assembly.     

CEMulti-core Architecture for Optimization of Energy over Heterogeneous Environment with High Performance Smart Sensor Devices

Nowadays, it is unusual for an electronic system to be without sensors, thus sensing plays an important part in everyday life. To this, the field of image processing is an added advantage as it stores image data and makes it readily available for parallel processing. The wireless sensor nodes over heterogeneous networks exhibit radio communication always with highest energy consumption. Multicore processors are more suitable for real time applications compared to traditional modern microcontrollers of sensor nodes in terms of improvement in their energy consumption rate. To significantly reduce the energy consumption, the usage of off-the-shelf low power microcontroller with appropriate processing core has to be considered. The proposed CEMulti-core architecture incorporated with the MIPS single core processor and multicore processor is simulated and the experimental results are compared and analyzed for their speedup, clock cycles and the time required for execution. Thus enabling the sensor node over heterogeneous networks to process large sized images with increase in energy efficiency.     

Identifying Neighbor and Connectivity of Wireless Sensor Networks with Poisson Point Process

Identifying neighbor and connectivity are the fundamental requirements in wireless sensor networks. The sensor nodes are scattered randomly over the area of interest and their first step is to identify their immediate neighbors, i.e., the nodes with which they have direct wireless communication. On the other hand, connectivity ensures that sensor nodes can communicate with each other in order to aggregate sensing data hop by hop to the base stations (sink nodes). In this paper, we study identifying neighbor and connectivity in the context of wireless sensor networks, using Poisson point process theory.     

A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network

The fundamental challenge for randomly deployed resource-constrained wireless sensor network is to enhance the network lifetime without compromising its performance metrics such as coverage rate and network connectivity. One way is to schedule the activities of sensor nodes and form scheduling rounds autonomously in such a way that each spatial point is covered by at least one sensor node and there must be at least one communication path from the sensor nodes to base station. This autonomous activity scheduling of the sensor nodes can be efficiently done with Reinforcement Learning (RL), a technique of machine learning because it does not require prior environment modeling. In this paper, a Nash Q-Learning based node scheduling algorithm for coverage and connectivity maintenance (CCM-RL) is proposed where each node autonomously learns its optimal action (active/hibernate/sleep/customize the sensing range) to maximize the coverage rate and maintain network connectivity. The learning algorithm resides inside each sensor node. The main objective of this algorithm is to enable the sensor nodes to learn their optimal action so that the total number of activated nodes in each scheduling round becomes minimum and preserves the criteria of coverage rate and network connectivity. The comparison of CCM-RL protocol with other protocols proves its accuracy and reliability. The simulative comparison shows that CCM-RL performs better in terms of an average number of active sensor nodes in one scheduling round, coverage rate, and energy consumption.

     

Communication architecture for processing spatio-temporal continuous queries in sensor networks

Wireless sensor networks have revolutionized distributed micro-sensing because of their ease of deployment, ad hoc connectivity and cost-effectiveness. They have also enabled collecting and monitoring data from a very large area or possibly several independent areas geographically separated from each other and such a process is known as spatio-temporal data monitoring. In this paper, we define an energy-aware routing infrastructure that enables distributed query processing and supports processing of spatio-temporal queries within the network. As operator execution demands high computation capability, we propose a possible use of a heterogeneous sensor network where query operators are assigned to sparsely-deployed resource-rich nodes within a dense network of low power sensor nodes. We have designed an adaptive, decentralized, low communication overhead algorithm to determine optimal operator placement on the resource-rich nodes such that data transfer cost in the network is minimized. To the best of our knowledge, this is the first attempt to build an energy-aware communication architecture to enable in-network processing of spatio-temporal queries.     

Cross-Layer Collaborative In-Network Processing in Multihop Wireless Sensor Networks

Emerging wireless sensor network (WSN) applications demand considerable computation capacity for in-network processing. To achieve the required processing capacity, cross-layer collaborative in-network processing among sensors emerges as a promising solution: sensors do not only process information at the application layer, but also synchronize their communication activities to exchange partially processed data for parallel processing. However, scheduling computation and communication events is a challenging problem in WSNs due to limited resource availability and shared communication medium. In this work, an application-independent task mapping and scheduling solution in multihop homogeneous WSNs, multihop task mapping and scheduling (MTMS), is presented that provides real-time guarantees. Using our proposed application model, the multihop channel model, and the communication scheduling algorithm, computation tasks and associated communication events are scheduled simultaneously. The dynamic voltage scaling (DVS) algorithm is presented to further optimize energy consumption. Simulation results show significant performance improvements compared with existing mechanisms in terms of minimizing energy consumption subject to delay constraints     

A distributed lightweight Redundancy aware Topology Control Protocol for wireless sensor networks

WSN consists of a large number of sensor nodes randomly deployed, and, in many cases, it is impossible to replace sensors when a node failure occurs. Thus, applications tend to deploy more nodes than necessary to cope with possible node failures and to increase the network lifetime, which leads to create some sensing and communication redundancy. However, sensors in the same region, may collect and forward the same information, which will waste more energy. In this paper, we propose a distributed Lightweight Redundancy aware Topology Control Protocol (LRTCP) for wireless sensor networks. It exploits the sensor redundancy in the same region by dividing the network into groups so that a connected backbone can be maintained by keeping a minimum of working nodes and turning off the redundant ones. LRTCP identifies equivalent nodes in terms of communication based on their redundancy degrees with respect of some eligibility rules. Simulation results indicate that, compared with existing distributed topology control algorithms, LRTCP improves network capacity and energy efficiency.     

Security in wireless sensor networks

Recent advances in electronics and wireless communication technologies have enabled the development of large-scale wireless sensor networks that consist of many low-power, low-cost, and small-size sensor nodes. Sensor networks hold the promise of facilitating large-scale and real-time data processing in complex environments. Security is critical for many sensor network applications, such as military target tracking and security monitoring. To provide security and privacy to small sensor nodes is challenging, due to the limited capabilities of sensor nodes in terms of computation, communication, memory/storage, and energy supply. In this article we survey the state of the art in research on sensor network security.     

The n-Hop Multilateration Primitive for Node Localization Problems

The recent advances in MEMS, embedded systems and wireless communication technologies are making the realization and deployment of networked wireless microsensors a tangible task. In this paper we study node localization, a component technology that would enhance the effectiveness and capabilities of this new class of networks. The n-hop multilateration primitive presented here, enables ad-hoc deployed sensor nodes to accurately estimate their locations by using known beacon locations that are several hops away and distance measurements to neighboring nodes. To prevent error accumulation in the network, node locations are computed by setting up and solving a global non-linear optimization problem. The solution is presented in two computation models, centralized and a fully distributed approximation of the centralized model. Our simulation results show that using the fully distributed model, resource constrained sensor nodes can collectively solve a large non-linear optimization problem that none of the nodes can solve individually. This approach results in significant savings in computation and communication, that allows fine-grained localization to run on a low cost sensor node we have developed.     

An efficient cluster-based communication protocol for wireless sensor networks

A wireless sensor network is a network of large numbers of sensor nodes, where each sensor node is a tiny device that is equipped with a processing, sensing subsystem and a communication subsystem. The critical issue in wireless sensor networks is how to gather sensed data in an energy-efficient way, so that the network lifetime can be extended. The design of protocols for such wireless sensor networks has to be energy-aware in order to extend the lifetime of the network because it is difficult to recharge sensor node batteries. We propose a protocol to form clusters, select cluster heads, select cluster senders and determine appropriate routings in order to reduce overall energy consumption and enhance the network lifetime. Our clustering protocol is called an Efficient Cluster-Based Communication Protocol (ECOMP) for Wireless Sensor Networks. In ECOMP, each sensor node consumes a small amount of transmitting energy in order to reach the neighbour sensor node in the bidirectional ring, and the cluster heads do not need to receive any sensed data from member nodes. The simulation results show that ECOMP significantly minimises energy consumption of sensor nodes and extends the network lifetime, compared with existing clustering protocol.     

Communication-efficient implementation of join in sensor networks

A sensor network is a multi-hop wireless network of sensor nodes cooperatively solving a sensing task. Each sensor node generates data items that are readings obtained from one or more sensors on the node. This makes a sensor network similar to a distributed database system. While this view is somewhat traditional, efficient execution of database (SQL) queries in sensor network remains a challenge, due to the unique characteristics of such networks such as limited memory and battery energy on individual nodes, multi-hop communication, unreliable infrastructure, and dynamic topology. Since the nodes are battery powered, the sensor network relies on energy-efficiency (and hence, communication efficiency) for a longer lifetime of the network.In this article, we have addressed the problem of communication-efficient implementation of the SQL “join” operator in sensor networks. In particular, we design an optimal algorithm for implementation of a join operation in dense sensor networks that provably incurs minimum communication cost under some reasonable assumptions. Based on the optimal algorithm, we design a suboptimal heuristic that empirically delivers a near-optimal join implementation strategy and runs much faster than the optimal algorithm. Through extensive simulations on randomly generated sensor networks, we show that our techniques achieve significant energy savings compared to other simple approaches.     

Asymptotic results for decentralized detection in power constrained wireless sensor networks

In this paper, we study a binary decentralized detection problem in which a set of sensor nodes provides partial information about the state of nature to a fusion center. Sensor nodes have access to conditionally independent and identically distributed observations, given the state of nature, and transmit their data over a wireless channel. Upon reception of the information, the fusion center attempts to accurately reconstruct the state of nature. Specifically, we extend existing asymptotic results about large sensor networks to the case where the network is subject to a joint power constraint, and where the communication channel from each sensor node to the fusion center is corrupted by additive noise. Large deviation theory is used to show that having identical sensor nodes, i.e., each node using the same transmission scheme, is asymptotically optimal. Furthermore, a performance metric by which sensor node candidates can be compared is established. We supplement the theory with examples to illustrate how the results derived in this paper apply to the design of practical sensing systems.     

Collaborative sensor networking towards real-time acoustical beamforming in free-space and limited reverberance

Wireless sensor networks have been attracting increasing research interest given the recent advances in microelectronics, array processing, and wireless networking. Consisting of a large collection of small, wireless, low-cost, integrated sensing, computing and communicating nodes capable of performing various demanding collaborative space-time processing tasks, wireless sensor network technology poses various unique design challenges, particularly for real-time operation. We review the approximate maximum-likelihood (AML) method for source localization and direction-of-arrival (DOA) estimation. Then, we consider the use of least-squares method (LS) method applied to DOA bearing crossings to perform source localization. A novel virtual array model applicable to the AML-DOA estimation method is proposed for reverberant scenarios. Details on the wireless acoustical testbed are given. We consider the use of Compaq iPAQ 3760s, which are handheld, battery-powered device normally meant to be used as personal organizers (PDAs), as sensor nodes. The iPAQ provide a reasonable balance of cost, availability, and functionality. It has a build in StrongARM processor, microphone, codec for acoustic acquisition and processing, and a PCMCIA bus for external IEEE 802.11b wireless cards for radio communication. The iPAQs form a distributed sensor network to perform real-time acoustical beamforming. Computational times and associated real-time processing tasks are described. Field measured results for linear, triangular, and square subarrays in free-space and reverberant scenarios are presented. These results show the effective and robust operation of the proposed algorithms and their implementations on a real-time acoustical wireless testbed.     

Adaptive and dual data-communication trust scheme for clustered wireless sensor networks

In wireless sensor networks, trust management schemes are designed to preserve them against misbehavior of malicious sensor nodes. These schemes observe the behavior of nodes, check their conformity to what is expected, compute and assign them trust values, and avoid any interaction with untrustworthy nodes. In this paper, we introduce Adaptive and dual Data-Communication Trust scheme (ADCT) for clustered wireless sensor networks to effectively deal with untrustworthy nodes. Unlike prior works, we propose an adaptive trust function to assess the direct trust between nodes according to the application’s requirement in terms of trust severity. We also consider data trust to cope with untrustworthy nodes during the data collection despite their communication capabilities. Moreover, we use the duality data-communication trust to deal with untrustworthy recommendations when building cluster-member’s feedback at the cluster-head level. Theoretical analysis and simulation show that the trust mechanism presented in this paper provides a better cooperation with the same or even lower communication overhead compared to the latest trust management schemes proposed for clustered wireless sensor networks.     

Survey on the design of underwater sensor nodes

Underwater wireless sensor networks are networks composed of various underwater sensor nodes (USNs) that are able to communicate with each other. The vast majority of Earth’s surface is composed of water, which makes such networks a very interesting research topic and enables a variety of applications, i.e, from oil monitoring to real time water pollution control. The design of USNs is paramount to the network’s operation. In comparison to terrestrial wireless sensor nodes, USNs are more expensive, larger, and present greater energy consumption, due to the harsh conditions of the aquatic environment. This leads to different challenges that need to be addressed in the design of the node, including processing, communications, energy management, data sensing, and storage. This survey aids in the development of underwater sensor nodes, and underwater applications. We present a general architecture of USNs and discuss the basic functions that must be accomplished by each unit. We also present a comprehensive study of all elements that compose a sensor node, including microcontrollers, memories, sensors, and batteries. In doing so, we highlight which aspects should be of pivotal importance in the design of a USN and how they affect communication protocols and applications. We believe that this survey can facilitate and guide development of future UWSN applications and protocols.     

Energy-efficient opportunistic coverage for people-centric urban sensing

Human-carried or vehicle-mounted sensors can be exploited to collect data ubiquitously for urban sensing. In this work, we study a new coverage problem, opportunistic coverage, to characterize the sensing quality of such people-centric sensing systems. Compared with the traditional static coverage and dynamic coverage in sensor networks, opportunistic coverage has some unique characteristics caused by the requirements of urban sensing applications and human mobility features such as spatio-temporal correlation, hotspots effects and randomness. In order to achieve good trade-off between energy consumption and coverage quality, we propose an offline node selection mechanism and an online adaptive sampling mechanism. The former can select the minimum number of nodes to achieve coverage requirements, based on the history trajectories of the given set of nodes, and the latter can help each selected node to decide whether to perform the sampling task at some time adaptively. Based on a real human mobility dataset and a taxi mobility dataset, extensive simulation results evaluate that our proposed models and mechanisms are effective and efficient in terms of energy consumption and coverage quality.     

Development of Energy Efficient Image/Video Sensor Networks

Image/Video Sensor Networks are emerging applications for sensor network technologies. The relatively high energy consuming image capturing process and the large size of the data collected by image/video sensors presents new challenges for the sensor network in terms of energy consumption and network capacity. We propose to address these issues through the use of a high density network deployment. A high density network allows network nodes to conserve power by reducing their transmission power and simultaneously increases the potential for spatially concurrent transmissions within the network, resulting in improved network throughput. Furthermore, with the use of additional relay nodes, we allow a communication density that differs from the sensing density. A higher communication density has the potential to further increase the spatially concurrent transmission. Moreover, this reduces the relay burden of the sensor node, thus conserving sensor energy. In this work, we show analytically how a high density network design effectively improves energy consumption and network capacity. Furthermore, we discuss the constraints placed on a high density sensor network deployment due to application latency requirements, sensor coverage requirements, connectivity requirements, and node costs.     

Analysis and modeling of task scheduling in wireless sensor network based on divisible load theory

In this paper, we propose a novel task scheduling algorithm (Divisible Task scheduling Algorithm for Wireless sensor networks (DTAW)) based on divisible load theory in heterogeneous wireless sensor networks to complete the tasks within the shortest possible time and reduce the sensors' energy‐consuming. In DTAW, the tasks are distributed to the wireless sensor network by the (SINK) on the basis of the processing and communication capacity of each sensor. After receiving the subtasks, the intracluster sensors carry out its tasks simultaneously and send the results to cluster head sequentially. By removing communication interference between each sensor, reduced task completion time and improved network resource utilization are achieved. Each cluster head simultaneously finishes sending fused data to the SINK after fusing the data obtained from intracluster sensors. In this way, the overlap between the task performing and communication phase would be much better. Simulation results are presented to demonstrate the impacts of different network parameters on the makespan and energy consumption. The results show that the algorithm enables to reasonably distribute tasks to each sensor and then effectively reduces the time‐consuming and energy‐consuming. Copyright © 2012 John Wiley & Sons, Ltd.     

XMSN: An efficient cross‐layer protocol for mixed wireless sensor networks

The application of wireless sensor networks (WSNs) technology in monitoring systems is demanding more efficient services to fulfill the requirements of the monitoring task. For this purpose, the simultaneous presence of features such as different communication mediums (air and water) used by nodes and various sizes of data generated by heterogeneous nodes are the key obstacles to build a communication protocol, which can ensure the reliable data delivery. This work terms such WSNs as mixed wireless sensor networks (MWSNs) which contains the aforementioned features. In this paper, we introduce a new cross‐layer protocol for mixed wireless sensor network (XMSN) which can adapt these features. The proposed cross layer protocol XMSN for such mixed environment is implemented and analyzed extensively in Castalia simulator. The performance of XMSN is compared with composition of well‐known protocols, namely, CTP plus BoX‐MAC‐2. The result shows that XMSN has better efficiency in terms of end‐to‐end delay, energy consumption, and goodput than that of CTP plus BoX‐MAC‐2 protocol.