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17
W paths in wireless sensor networks
 Proceedings of MSN 2005
, 2005
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Maximizing aggregated revenue in sensor networks under deadline constraints
 in CDC
, 2009
"... Abstract—We study the problem of maximizing the aggregated revenue in sensor networks with deadline constraints. Our model is that of a sensor network that is arranged in the form of a tree topology, where the root corresponds to the sink node, and the rest of the network detects an event and trans ..."
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Abstract—We study the problem of maximizing the aggregated revenue in sensor networks with deadline constraints. Our model is that of a sensor network that is arranged in the form of a tree topology, where the root corresponds to the sink node, and the rest of the network detects an event and transmits data to the sink over one or more hops. We assume a timeslotted synchronized system and a nodeexclusive (also called a primary) interference model. We formulate this problem as an integer optimization problem and show that the optimal solution involves solving a Bipartite Maximum Weighted Matching problem at each hop. We propose a polynomial time algorithm based on dynamic programming that uses only local information at each hop to obtain the optimal solution. Thus, we answer the question of when a node should stop waiting to aggregate data from its predecessors and start transmitting in order to maximize revenue within a deadline imposed by the sink. Further, we show that our optimization framework is general enough that it can be extended to a number of interesting cases such as incorporating sleepwake scheduling, minimizing aggregate sensing error, etc. I.
Energyefficient selective forwarding for sensor networks
 in Proc. Workshop on Energy in Wireless Sensor Networks (WEWSN’08), in conjunction with DCOSS’08
, 2008
"... Abstract—In this paper a new energyefficient scheme for data transmission in wireless sensor networks is proposed. It is based on the idea of selective forwarding: sensor nodes only transmit the most relevant messages, discarding the least important ones. To do so, messages are assumed to be graded ..."
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Abstract—In this paper a new energyefficient scheme for data transmission in wireless sensor networks is proposed. It is based on the idea of selective forwarding: sensor nodes only transmit the most relevant messages, discarding the least important ones. To do so, messages are assumed to be graded with an importance value, and a forwarding threshold, which depends on the sensor consumption patterns, the available energy resources and the information obtained from the neighborhood, is applied to these values. In this approach, the sensor decision also depends on the expected behavior of neighboring nodes, so as to maximize not only the transmission efficiency, but also the performance of the whole communication up to the destination node. Simulation results show that the proposed scheme increases the network lifetime, and maximizes the global importance of the messages received by the sink node. Index Terms—selective forwarding, energyefficiency, message importance, sensor networks
When InNetwork Processing Meets Time: Complexity and Effects of Joint Optimization in Wireless Sensor Networks
"... Abstract—As sensornets are increasingly being deployed in missioncritical applications, it becomes imperative that we consider application QoS requirements in innetwork processing (INP). Towards understanding the complexity of joint QoS and INP optimization, we study the problem of jointly optimiz ..."
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Abstract—As sensornets are increasingly being deployed in missioncritical applications, it becomes imperative that we consider application QoS requirements in innetwork processing (INP). Towards understanding the complexity of joint QoS and INP optimization, we study the problem of jointly optimizing packet packing (i.e., aggregating shorter packets into longer ones) and the timeliness of data delivery. We identify the conditions under which the problem is strong NPhard, and we find that the problem complexity heavily depends on aggregation constraints (in particular, maximum packet size and reaggregation tolerance) instead of network and traffic properties. For cases when the problem is NPhard, we show that there is no polynomialtime approximation scheme (PTAS); for cases when the problem can be solved in polynomial time, we design polynomial time, offline algorithms for finding the optimal packet packing schemes. To understand the impact of joint QoS and INP optimization on sensornet performance, we design a distributed, online protocol tPack that schedules packet transmissions to maximize the local utility of packet packing at each node. Using a testbed of 130 TelosB motes, we experimentally evaluate the properties of tPack. We find that jointly optimizing data delivery timeliness and packet packing significantly improve network performance. Our findings shed light on the challenges, benefits, and solutions of joint QoS and INP optimization, and they also suggest open problems for future research. KeywordsWireless network, sensor network, realtime, packet packing, innetwork processing I.
LBA: Lifetime Balanced Data Aggregation in Low Duty Cycle Sensor Networks
"... Abstract—This paper proposes LBA, a lifetime balanced data aggregation scheme for asynchronous and duty cycle sensor networks under an applicationspecific requirement of endtoend data delivery delay bound. In contrast to existing aggregation schemes that focus on reducing the energy consumption a ..."
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Abstract—This paper proposes LBA, a lifetime balanced data aggregation scheme for asynchronous and duty cycle sensor networks under an applicationspecific requirement of endtoend data delivery delay bound. In contrast to existing aggregation schemes that focus on reducing the energy consumption and extending the operational lifetime of each individual node, LBA has a unique design goal to balance the nodal lifetime and thus prolong the network lifetime more effectively. To achieve this goal in a distributed manner, LBA adaptively adjusts the aggregation holding time between neighboring nodes to balance their nodal lifetime; as such balancing take place in all neighborhoods, nodes in the entire network can gradually adjust their nodal lifetime towards the globally balanced status. Experimental studies on a sensor network testbed shows that LBA can achieve the design goal, yield longer network lifetime than other nonadaptive and nodal lifetimeunaware data aggregation schemes, and approach the theoretical upperbound performance, especially when nodes have highly different nodal lifetime. A. Motivations I.
Deadline Constrained Scheduling for Data Aggregation in Unreliable Sensor Networks
"... AbstractWe study the problem of maximizing the aggregated information in a wireless sensor network. We consider a sensor network with a tree topology, where the root corresponds to the sink, and the rest of the network detects an event and transmits data to the sink. We formulate an integer optimi ..."
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AbstractWe study the problem of maximizing the aggregated information in a wireless sensor network. We consider a sensor network with a tree topology, where the root corresponds to the sink, and the rest of the network detects an event and transmits data to the sink. We formulate an integer optimization problem that maximizes the aggregated information that reaches the sink under deadline and interference constraints. This framework allows using a variety of error recovery schemes to tackle link unreliability. We show that the optimal solution involves solving a Job Interval Selection Problem (JISP) which is known to be MAX SNPHard. We construct a suboptimal version, and develop a low complexity, distributed optimal solution to this version. We investigate tree structures for which this solution is optimal to the original problem. Our numerical results show that the suboptimal solution outperforms existing JISP approximation algorithms even for general trees.
Maximizing Information in Unreliable Sensor Networks Under Deadline and Energy Constraints
 IEEE Transactions on Automatic Control
, 2013
"... AbstractWe study the problem of maximizing the information in a wireless sensor network with unreliable links. We consider a sensor network with a tree topology, where the root corresponds to the sink, and the rest of the network detects an event and transmits data to the sink. We formulate a comb ..."
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AbstractWe study the problem of maximizing the information in a wireless sensor network with unreliable links. We consider a sensor network with a tree topology, where the root corresponds to the sink, and the rest of the network detects an event and transmits data to the sink. We formulate a combinatorial optimization problem that maximizes the information that reaches the sink under deadline, energy, and interference constraints. This framework allows using a variety of error recovery schemes to tackle link unreliability. We show that this optimization problem is NPhard in the strong sense when the input is the maximum node degree of the tree. We then propose a dynamic programming framework for solving the problem exactly, which involves solving a special case of the Job Interval Selection Problem (JISP) at each node. Our solution has a polynomial time complexity when the maximum node degree is O(log N ) in a tree with N nodes. For trees with higher node degrees, we further develop a suboptimal solution, which has low complexity and allows distributed implementation. We investigate tree structures for which this solution is optimal to the original problem. The efficiency of the suboptimal solution is further demonstrated through numerical results on general trees.
On Optimal Energy Efficient Convergecasting in Unreliable Sensor Networks with Applications to Target Tracking.
 In Proc. of ACM MobiHoc,
, 2011
"... ABSTRACT In this paper, we develop a mathematical framework for studying the problem of maximizing the "information" received at the sink in a data gathering wireless sensor network. We explicitly account for unreliable links, energy constraints, and innetwork computation. The network mo ..."
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ABSTRACT In this paper, we develop a mathematical framework for studying the problem of maximizing the "information" received at the sink in a data gathering wireless sensor network. We explicitly account for unreliable links, energy constraints, and innetwork computation. The network model is that of a sensor network arranged in the form of a tree topology, where the root corresponds to the sink node, and the rest of the network detects an event and transmits data to the sink over one or more hops. This problem of sending data from multiple sources to a common sink is often referred to as the convergecasting problem. We develop an integer optimization based framework for this problem, which allows for tackling link unreliability using general errorrecovery schemes. Even though this framework has a nonlinear objective function, and cannot be relaxed to a convex programming problem, we develop a low complexity, distributed solution. The solution involves finding a Maximum Weight Increasing Independent Set (MWIIS) in rectangle graphs over each hop of the network, and can be obtained in polynomial time. Further, we apply these techniques to a target tracking problem where we optimally select sensors to track a given target such that the information obtained is maximized subject to constraints on the pernode sensing and communication energy. We validate our algorithms through numerical evaluations, and illustrate the advantages of explicitly considering link unreliability in the optimization framework.
Measurement Aggregation and Routing Techniques for EnergyEfficient Estimation in Wireless Sensor Networks
"... Abstract—Wireless sensor networks are fundamentally different from other wireless networks due to energy constraints and spatial correlation among sensor measurements. Mechanisms that efficiently compress and transport sensor data in the network are needed. We consider the problem of maximizing li ..."
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Abstract—Wireless sensor networks are fundamentally different from other wireless networks due to energy constraints and spatial correlation among sensor measurements. Mechanisms that efficiently compress and transport sensor data in the network are needed. We consider the problem of maximizing lifetime of wireless sensor networks that are entitled with the task of estimating an unknown parameter or process and thus need to adhere to estimation error specifications. We investigate optimal endogenous sensor measurement rate control, innetwork data aggregation and routing for achieving the goal above. Sensors take measurements and aggregate incoming data from neighbors in a single outgoing flow by applying appropriate aggregation weights. By doing so, they control the variance of outgoing flow. Each sensor controls its measurement rate and aggregation weights, and aggregated measurement data are routed to the FC for Maximum Likelihood (ML) estimation. The challenge is to find an optimal compromise between eliminating data redundancy and maintaining data representation accuracy so as to adhere to estimation quality constraints and reduce the volume of transported data, thus improving network lifetime. Sensor spatial correlation, measurement accuracies, link qualities and energy reserves affect sensor measurement rates, data aggregation and routes to the FC. On the other hand, measurement rates, aggregation, and sensor characteristics impact the estimation error. We show that the problem can be decomposed into separate optimization problems where each sensor autonomously takes its measurement rate, aggregation and routing decisions. We design an iterative primaldual algorithm that relies on low overhead feedback from the FC to the nearest sensors, and on sensor neighbor Lagrange multiplier exchanges. Our work strikes the optimal fundamental tradeoff between network lifetime, innetwork data aggregation and estimation quality and yields a solution based on distributed sensor coordination. I.
Joint Aggregation and MAC Design to Prolong Sensor Network Lifetime
"... MAC design, to improve the sensor network lifetime under the endtoend delay constraint. The key idea is to adjust both network traffic (via data aggregation) and communication overhead (via dutycycled MAC) in a holistic manner at each individual node as well as between neighbors. As a result, JAM ..."
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MAC design, to improve the sensor network lifetime under the endtoend delay constraint. The key idea is to adjust both network traffic (via data aggregation) and communication overhead (via dutycycled MAC) in a holistic manner at each individual node as well as between neighbors. As a result, JAM extends the sensor network lifetime more efficiently and effectively than the stateoftheart solutions while guaranteeing the desired delay bound and achieving a lower level of average nodal power consumption. JAM is a lightweight and distributed solution with limited control information exchanged between neighbors only, which makes it deployable in practical sensor networks. Extensive ns2 simulation and TinyOS experiment results are used to demonstrate the effectiveness of JAM in prolonging the network lifetime.