As new fabrication and integration technologies reduce the cost and size of micro-sensors and wireless interfaces, it becomes feasible to deploy densely distributed wireless networks of sensors and actuators. These systems promise to revolutionize biological, earth, and environmental monitoring applications, providing data at granularities unrealizable by other means. In addition to the challenges of miniaturization, new system architectures and new network algorithms must be developed to transform the vast quantity of raw sensor data into a manageable stream of high-level data. To address this, we propose a tiered system architecture in which data collected at numerous, inexpensive sensor nodes is filtered by local processing on its way through to larger, more capable and more expensive nodes.We briefly describe Habitat monitoring as our motivating application and introduce initial system building blocks designed to support this application. The remainder of the paper presents details of our experimental platform.

Habitat monitoring: Application driver for wireless communications technology

Machine learning algorithms for wireless sensor networks: A survey

Several studies have demonstrated the benefits of using a mobile sink to reduce the energy consumption of nodes and to prevent the formation of energy holes in wireless sensor networks (WSNs). However, these benefits are dependent on the path taken by the mobile sink, particularly in delay-sensitive applications, as all sensed data must be collected within a given time constraint. An approach proposed to address this challenge is to form a hybrid moving pattern in which a mobile-sink node only visits rendezvous points (RPs), as opposed to all nodes. Sensor nodes that are not RPs forward their sensed data via multihopping to the nearest RP. The fundamental problem then becomes computing a tour that visits all these RPs within a given delay bound. Identifying the optimal tour, however, is an NP-hard problem. To address this problem, a heuristic called weighted rendezvous planning (WRP) is proposed, whereby each sensor node is assigned a weight corresponding to its hop distance from the tour and the number of data packets that it forwards to the closest RP. WRP is validated via extensive computer simulation, and our results demonstrate that WRP enables a mobile sink to retrieve all sensed data within a given deadline while conserving the energy expenditure of sensor nodes. More specifically, WRP reduces energy consumption by 22% and increases network lifetime by 44%, as compared with existing algorithms.

An Energy-Efficient Mobile-Sink Path Selection Strategy for Wireless Sensor Networks

Today, large numbers of smart interconnected devices provide safety and security critical services for energy grids, industrial control systems, gas and oil search robots, home/office automation, transportation, and critical infrastructure. These devices often operate in swarms -- large, dynamic, and self-organizing networks. Software integrity verification of device swarms is necessary to ensure their correct and safe operation as well as to protect them against attacks. However, current device attestation schemes assume a single prover device and do not scale to swarms. We present SEDA, the first attestation scheme for device swarms. We introduce a formal security model for swarm attestation and show security of our approach in this model. We demonstrate two proof-of-concept implementations based on two recent (remote) attestation architectures for embedded systems, including an Intel research platform. We assess performance of SEDA based on these implementations and simulations of large swarms. SEDA can efficiently attest swarms with dynamic and static topologies common in automotive, avionic, industrial control and critical infrastructures settings.

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SEDA: Scalable Embedded Device Attestation

Monitoring of environmental phenomena with embedded networked sensing confronts the challenges of both unpredictable variability in the spatial distribution of phenomena, coupled with demands for a high spatial sampling rate in three dimensions. For example, low distortion mapping of critical solar radiation properties in forest environments may require two-dimensional spatial sampling rates of greater than 10 samples/m² over transects exceeding 1000 m². Clearly, adequate sampling coverage of such a transect requires an impractically large number of sensing nodes. This paper describes a new approach where the deployment of a combination of autonomous-articulated and static sensor nodes enables sufficient spatiotemporal sampling density over large transects to meet a general set of environmental mapping demands. To achieve this we have developed an embedded networked sensor architecture that merges sensing and articulation with adaptive algorithms that are responsive to both variability in environmental phenomena discovered by the mobile sensors and to discrete events discovered by static sensors. We begin by describing the class of important driving applications, the statistical foundations for this new approach, and task allocation. We then describe our experimental implementation of adaptive, event aware, exploration algorithms, which exploit our wireless, articulated sensors operating with deterministic motion over large areas. Results of experimental measurements and the relationship among sampling methods, event arrival rate, and sampling performance are presented.

Call and Response: Experiments in Sampling the Environment

Several studies in recent years have considered the use of mobile elements for data gathering in wireless sensor networks, so as to reduce the need for multi-hop forwarding among the sensor nodes and thereby prolong the network lifetime. Since, typically, practical constraints preclude a mobile element from visiting all nodes in the sensor network, the solution must involve a combination of a mobile element visiting a subset of the nodes (cache points), while other nodes communicate their data to the cache points wirelessly. This leads to the optimization problem of minimizing the communication distance of the sensor nodes, while keeping the tour length of the mobile element below a given constraint. Several algorithms in existing literature have tackled this problem by separating the construction of the mobile element tour from the computation of the forwarding trees to the cache points. In this paper, we propose a new algorithm that alternates between these phases and iteratively improves the outcome of each phase, based on the result of the other. We compare the resulting performance of our algorithm with that of previous work, and show that it closes a considerable portion of the gap from the theoretical optimal solution.

Energy-efficient data gathering with tour length-constrained mobile elements in wireless sensor networks

We consider the problem of gathering data from a sensor network using mobile elements. In particular, we consider the case where the data are produced by measurements and need to be delivered to a predefined sink within a given time interval from the time the measurement takes place. Mobile elements travel the network in predefined paths, collect the data from the nodes, and deliver them to the sink. Each node must be visited by a mobile element that must then reach the sink within the given time constraint. The goal is to plan the paths for the mobile elements that minimize the total length travelled. Several variations of this problem have been considered in existing literature. We propose an algorithmic solution that builds node-disjoint tours that always include the sink, cover the network, and optimize the total length travelled. We provide an integer linear programming formulation for the problem, and propose two novel heuristics for building the tours. We evaluate the performance of our algorithm by comparing it to the optimal solution as well as to an alternative heuristic, commonly used in related time-window vehicle routing problems, and demonstrate the superior performance of our approach.

Mobile Element Path Planning for Time-Constrained Data Gathering in Wireless Sensor Networks

Wireless sensor networks (WSNs) are a core infrastructure for automatic environmental monitoring We developed Corona as an in-network distributed query processor that allows to share a sensor network between several users with a declarative query language It includes a novel approach for minimising sensor activations in shared wireless sensor networks: we introduce the notion of freshness into WSN so that users can ask for cached sensor reading with freshness guarantees We further integrated a resource-awareness framework that allows the query processor to dynamically adapt to changing resource levels The capabilities of this system are demonstrated with several aggregation queries for different users with different freshness and result precision needs

Corona: energy-efficient multi-query processing in wireless sensor networks

Food rescue organizations collect and re-distribute surplus perishable food for hunger relief. We propose novel approaches to address this humanitarian logistics challenge and find envy-free allocations of the rescued food together with least travel cost routes. We show that this food rescue and delivery problem is NP-hard and we present a cutting-plane algorithm based on Benders’ decomposition for its exact solution. We introduce a novel heuristic algorithm that combines greedy and local search. We test our approaches using real data from food rescue organizations. Our results show that the proposed algorithms are able to efficiently provide envy-free and cost-effective solutions.

Exact and heuristic algorithms for finding envy-free allocations in food rescue pickup and delivery logistics

We consider a well-established model of data gathering in wireless sensor networks WSNs that combines mobile elements visiting some of the nodes, and wireless communication from the rest. Since wireless communication impacts the life-time of the network, there is a need to design the mobile element path so that the required communication is minimised, while the path length is bounded in length. Based on the required structure of the tour we investigate two variations of this problem. First, we consider mobile element tours that start and end at a predefined sink. Then we consider the unrestricted case, where the path is not required to return to the predefined sink node. We propose new algorithms that alternate between the path design and the multi-hop wireless communication routes, and iteratively improve the out-come of each phase. We compare the resulting performance of our algorithms with the best known comparable schemes in the literature.

Khaled Almi'ani

Papers

Energy-efficient data gathering with tour length-constrained mobile elements in wireless sensor networks

Mobile Element Path Planning for Time-Constrained Data Gathering in Wireless Sensor Networks

Corona: energy-efficient multi-query processing in wireless sensor networks

Exact and heuristic algorithms for finding envy-free allocations in food rescue pickup and delivery logistics

Tour and path planning methods for efficient data gathering using mobile elements