In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.

The possibility to employ reaction-diffusion models to build spatial patterns in sensor networks has been advocated in other works. Nevertheless it has not been investigated how the biologically-inspired solutions perform in comparison to more traditional ones taking into account specificities of sensor networks like severe energy constraints. In this paper we present some preliminary results on the comparison between a biologically inspired coordination mechanism based on activator-inhibitor interaction and a simple mechanism, where nodes do not communicate but activate their sensing circuitry according to some probability.

In recent years, the use of wireless sensor networks for industrial applications has rapidly increased. However, energy consumption still remains one of the main limitations of this technology. As communication typically accounts for the major power consumption, the activity of the transceiver should be minimized, in order to prolong the network lifetime. To this end, this paper proposes an Adaptive Staggered sLEEp Protocol (ASLEEP) for efficient power management in wireless sensor networks targeted to periodic data acquisition. This protocol dynamically adjusts the sleep schedules of nodes to match the network demands, even in time-varying operating conditions. In addition, it does not require any a-priori knowledge of the network topology or traffic pattern. ASLEEP has been extensively studied with simulation. The results obtained show that, under stationary conditions, the protocol effectively reduces the energy consumption of sensor nodes (by dynamically adjusting their duty-cycle to current needs) thus increasing significantly the network lifetime. With respect to similar non-adaptive solutions, it also reduces the average message latency and may increase the delivery ratio. Under timevarying conditions the protocol is able to adapt the duty-cycle of single nodes to the new operating conditions while keeping a consistent sleep schedule among sensor nodes. The results presented here are also confirmed by an experimental evaluation in a real testbed.

Summary. Networked embedded sensor systems use the evidence gathered by spatially distributed heterogeneous sensor nodes possessing partial and different scopes of expertise, to make inferences on the scenario under observation. For such system to make an accurate collective decision, the partial and incomplete evidence provided by nodes must be processed in a simple and straightforward manner during the information exchange and fusion process. To achieve these objectives, we present a novel, unified approach named “non-recursive evidence filtering ” based on the Dempster-Shafer (DS) formalism for evidence representation. It is capable of selectively fusing partial evidence in such a network to directly infer on events of interest such as threats occurring with a certain temporal distribution, while accommodating the varying reliability and accuracy of information sources. Certain restrictions on the coefficients impose several challenges in the design of such evidence filters. We show that the gain of these evidence filters at frequency zero is always equal to one and all coefficients must be non-negative. This suggests that arbitrary frequency shaping is not possible and a pure bandpass evidence filter is not realizable. A method to design a simple FIR evidence filter to detect periodic events of interest is presented. Multi-dimensional spatio-temporal evidence filters are discussed as a direct extension to the above, along with a low signature threat detection example that clearly illustrates the effectiveness of the evidence filtering concept in distributed networked embedded systems. 1

Wireless sensor network (WSN) applications have been studied extensively in recent years. Such applications involve resource-limited embedded sensor nodes that have small size and low power requirements. Based on the need for extended network lifetimes in WSNs in terms of energy use, the energy efficiency of computation and communication operations in the sensor nodes becomes critical. Digital signal processing (DSP) applications typically require intensive data processing operations and as a result are difficult to implement directly in resource-limited WSNs. In this paper, we present a novel design methodology for modeling and implementing computationallyintensive DSP applications applied to wireless sensor networks. This methodology explores efficient modeling techniques for DSP applications, including data sensing and processing; derives formulations of energy-driven partitioning (EDP) for distributing such applications across wireless sensor networks; and develops efficient heuristic algorithms for finding partitioning results that maximize the network lifetime. To address such an energy-driven partitioning problem, this paper provides a new way of aggregating data and reducing communication traffic among nodes based on application analysis. By considering low data token delivery points and the distribution of computation in the application, our approach finds energy-efficient trade-offs between data communication and

Wireless sensor networks consist of resourceconstrained nodes; especially with respect to power resources. In many cases, the replacement of a dead node is difficult and costly, e.g. an implanted node in the human body. Our main goal in this paper is reducing the total power consumption of the network. For this purpose, we consider the cooperation of nodes in data transmission in terms of a group, since the major consumer of power is the data transmission process. A mobile node may move to a new location, in which it is desirable for the node to join a group. In this paper, we propose an algorithm for nodes to choose the best group in their signal range, using coalitional game theory to determine what is beneficial in terms of power consumption. The protocol is formalized in rewriting logic, implemented in the Maude tool, and validated by means of Maude’s model exploration facilities. Simulation-based tools are in general not able to prove the protocol. However, by using Maude, we prove the correctness of our proposed protocol, by searching for failures of the protocol, through all possible behaviors of sensors. These searches prove that grouping nodes is done correctly in all reachable states from a set of initial states of the model. In addition, we simulate our model in order to quantitatively analyze the efficiency of the proposed protocol. The results show significant improvements in power efficiency. 1.

Distributed embedded computing systems are special-purpose computer systems designed for particular applications and set up in a networked or distributed manner. A practical application domain for such a distributed system setup is the domain of wireless sensor network (WSN) applications. In this thesis, studies of architectures, applications, and methodologies for distributed embedded systems will be covered by addressing a number of important energy and performance optimization problems for translating highlevel representations of distributed embedded applications into system implementations. This thesis is also concerned about systematic design methodologies and optimization problems for both software and hardware implementations. With advances in integrated circuit technology, distributed embedded platforms such as wireless sensor nodes can be equipped with increasing amounts of computational resources, such as digital signal processing (DSP) subsystems that can handle intensivecomputational tasks. By incorporating such dedicated DSP processing, a distributed embedded platform can enhance its functional capabilities for processing data before transmitting the data to other parts of the network or to an associated base station (central node). Therefore, this thesis presents a design methodology for distributing DSP applications

Researchers in wireless Micro-sensor networks (M-WSN) have proposed various protocols for energy conservations. Each protocol is aimed to optimize power utilization in wireless sensor networks. The different protocol approaches optimize power consumption either considering their application areas or network topologies. This paper studies various energy-saving protocols for micro sensor networks and presents their brief classifications. An M-WSN is a group of hundreds or thousands of small energy-limited sensors that are densely deployed in a large geographical region. These micro sensors in sensor network are autonomous devices responsible for forwarding locally collected data to a central node called sink node by following multi-hop wireless paths. The sink node performs data fusion to form a single meaningful result. The micro sensors work by utilizing limited battery-power, the considerable part is to use this power for a maximum time. Although recent developments in electronics has enabled the development of low-cost & low-power sensors networks, still there is a challenging job of energy conservation optimization within the wireless sensor networks (WSNs).

Wireless sensor networks consist of resource-constrained nodes; especially with respect to power resources. Often, the replacement of a dead node is difficult and costly; e.g. a node may be implanted in the human body. Therefore, it is important to reduce the total power consumption of WSNs. The major consumer of power is the data transmission process. This paper considers nodes which cooperate in data transmission in terms of a group. A mobile node may move to a new location, in which it is desirable for the node to join a group. We propose a protocol to allow nodes to choose the best group in their signal range, using coalitional game theory to determine what is beneficial in terms of power consumption. The protocol is formalized as an SOS-style transition system. This formalization forms the basis for an implementation in the rewriting logic tool Maude, so the protocol can be validated using Maude’s model exploration facilities. First, we prove the correctness of our proposed protocol, by searching for failures through all possible behaviors for given initial states. For these searches, the grouping is done correctly in all reachable final states of the model. Second, we simulate the model behavior to quantitatively analyze the efficiency of the proposed protocol. The results show significant improvements in power efficiency. This work was done in the context of the project Connect funded by the Norwegian Research Council and the EU project FP7-231620 HATS.