Data Aggregation Routing Research Articles

Vehicle Routing for Attended Home Delivery in City Logistics

Cost-efficient and sustainable delivery of e-commerce products results in enormous challenges for city logistics service providers. We focus on the last mile of delivery in terms of reliable and efficient vehicle routing approaches. In recent years, telematics based traffic data collection has built the ground for time-dependent vehicle routing, which supports the fulfillment of customer promises as well as economic and sustainable delivery concepts. This becomes extremely important in Attended Home Delivery applications, requiring the presence of the customer during delivery. Based on Floating Car Data and Data Mining methods, we provide time-dependent travel time data sets and discuss the integration of time-dependent travel times in time-dependent vehicle routing models. A case study offers insights into effort and benefits of sophisticated travel time collection, data aggregation and time-dependent vehicle routing.

Mobile Element Scheduling for Efficient Data Collection in Wireless Sensor Networks: A Survey

Problem statement: Wireless Sensor Networks (WSN) has become a very interesting field of research and has been deployed for various applications. The data sensed by the sensors need to be transmitted over to a Base Station (BS) where the data processing is done. Approach: The existing systems generally employ Multi-hop routing for data aggregation at the BS, where several nodes may forward data packets to the BS. This will lead to the reduced lifetime of the network and reduced battery life for sensor nodes near the BS, as they have to relay data from all parts of the network to the BS. Results: A simple yet efficient method to improve the network lifetime is designed with the help of Mobile Element (ME). The MEs act as mechanical carriers which move around in the sensing field, collecting the data from the sensors and transmitting them to the BS. In a relatively larger network, a single mobile element might not serve the purpose. So Rendezvous Points (RP) can be used in the network, to enhance the performance of the WSN. Conclusion/Recommendations: This article discusses the state of the art and major research challenges in scheduling these ME and the arising need for multiple MEs also being outlined.

Delay QoS and MAC Aware Energy-Efficient Data-Aggregation Routing in Wireless Sensor Networks

By eliminating redundant data flows, data aggregation capabilities in wireless sensor networks could transmit less data to reduce the total energy consumption. However, additional data collisions incur extra data retransmissions. These data retransmissions not only increase the system energy consumption, but also increase link transmission delays. The decision of when and where to aggregate data depends on the trade-off between data aggregation and data retransmission. The challenges of this problem need to address the routing (layer 3) and the MAC layer retransmissions (layer 2) at the same time to identify energy-efficient data-aggregation routing assignments, and in the meantime to meet the delay QoS. In this paper, for the first time, we study this cross-layer design problem by using optimization-based heuristics. We first model this problem as a non-convex mathematical programming problem where the objective is to minimize the total energy consumption subject to the data aggregation tree and the delay QoS constraints. The objective function includes the energy in the transmission mode (data transmissions and data retransmissions) and the energy in the idle mode (to wait for data from downstream nodes in the data aggregation tree). The proposed solution approach is based on Lagrangean relaxation in conjunction with a number of optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithm outperforms existing heuristics that do not take MAC layer retransmissions and the energy consumption in the idle mode into account.

Optimization-Based Channel Constrained Data Aggregation Routing Algorithms in Multi-Radio Wireless Sensor Networks

In wireless sensor networks, data aggregation routing could reduce the number of data transmissions so as to achieve energy efficient transmission. However, data aggregation introduces data retransmission that is caused by co-channel interference from neighboring sensor nodes. This kind of co-channel interference could result in extra energy consumption and significant latency from retransmission. This will jeopardize the benefits of data aggregation. One possible solution to circumvent data retransmission caused by co-channel interference is to assign different channels to every sensor node that is within each other's interference range on the data aggregation tree. By associating each radio with a different channel, a sensor node could receive data from all the children nodes on the data aggregation tree simultaneously. This could reduce the latency from the data source nodes back to the sink so as to meet the user's delay QoS. Since the number of radios on each sensor node and the number of non-overlapping channels are all limited resources in wireless sensor networks, a challenging question here is to minimize the total transmission cost under limited number of non-overlapping channels in multi-radio wireless sensor networks. This channel constrained data aggregation routing problem in multi-radio wireless sensor networks is an NP-hard problem. I first model this problem as a mixed integer and linear programming problem where the objective is to minimize the total transmission subject to the data aggregation routing, channel and radio resources constraints. The solution approach is based on the Lagrangean relaxation technique to relax some constraints into the objective function and then to derive a set of independent subproblems. By optimally solving these subproblems, it can not only calculate the lower bound of the original primal problem but also provide useful information to get the primal feasible solutions. By incorporating these Lagrangean multipliers as the link arc weight, the optimization-based heuristics are proposed to get energy-efficient data aggregation tree with better resource (channel and radio) utilization. From the computational experiments, the proposed optimization-based approach is superior to existing heuristics under all tested cases.

Integrated channel assignment and data aggregation routing problem in wireless sensor networks

In wireless sensor networks, data aggregation routing could reduce the number of data transmission so as to achieve efficient total energy consumption. However, this kind of data aggregation introduces data retransmission that is caused by co-channel interference from neighbouring sensor nodes. Hence, more data aggregation leads to more extra energy consumption and significant retransmission delay from retransmission. This could jeopardise the benefits of data aggregation. One possible solution to circumvent retransmission caused by co-channel interference is to assign different channel to every sensor node that is within each other's interference range on the data aggregation tree. As the number of non-overlapping channels is limited in wireless networks, it is unlikely that we could assign a different channel to every sensor node on the data aggregation tree. Then, an interesting problem is to perform data aggregation routing in conjunction with channel assignment to minimise total transmission power under limited number of non-overlapping channels. This problem is an NP-complete problem. We devise heuristic algorithm, Iterative Channel Adjustment Data Aggregation Routing algorithm (ICADAR), and other three heuristics, to tackle this problem. From the simulation results, the ICADAR algorithm outperforms the other three algorithms under all experimental cases.

Cooperative routing for distributed detection in large sensor networks

In this paper, the detection of a correlated Gaussian field using a large multi-hop sensor network is investigated. A cooperative routing strategy is proposed by introducing a new link metric that characterizes the detection error exponent. Derived from the Chernoff information and Schweppe's likelihood recursion, this link metric captures the contribution of a given link to the decay rate of error probability and has the form of the capacity of a Gaussian channel with the sender transmitting the innovation of its measurement. For one-dimensional Gauss-Markov fields, the link metric can be represented explicitly as a function of the link length. Cooperative routing is achieved using the Kalman data aggregation and shortest path routing. Numerical simulations show that cooperative routing can be significantly more energy efficient than noncooperative routing for the same detection performance

Multi-sink data aggregation routing and scheduling with dynamic radii in WSNs

In wireless sensor networks (WSNs) with multiple sink nodes, energy is wasted on idle listening, redundant transmissions, and unnecessary power consumption. The total energy consumption may be minimized by properly scheduling communications in conjunction with aggregating data and dynamically adjusting radii. We propose near-optimal data aggregation routing and duty cycle scheduling heuristics, denoted by MDAR and O-MAC, which achieve energy efficiency and bound latency within a reasonable range. These heuristics outperform other general data aggregation routing heuristics (e.g., CNS, GIT, and SPT) and scheduling protocols (e.g., S-MAC and T-MAC) by 7%-45%, according to our experimental results.

Energy efficient routing and scheduling for real-time data aggregation in WSNs

In this paper, we study the problem of constructing an energy efficient data aggregation tree for data gathering in wireless sensor networks. We consider a real-time scenario where the data aggregation must be performed within a specified latency constraint. The objective is to minimize the overall energy cost of the sensor nodes in the aggregation tree subject to the latency constraint. We propose an algorithm that can construct a data aggregation tree with theoretically bounded energy cost under the latency constraint. Extensive simulations show that the proposed algorithm is very efficient in energy-saving under the latency constraint and has better performance than other existing algorithms.

Avoid 'void' in geographic routing for data aggregation in sensor networks

Wireless sensor networks have attracted great attention in research and industrial development due to their fast-growing application potentials. New techniques must be developed for sensor networks due to their lack of infrastructure support and the constraints on computation capability, memory space, communication bandwidth and above all, energy supply. To prolong the life time of a battery-powered sensor network, an energy efficient routing algorithm for data collection is essential. We propose a new geographic routing algorithm that forwards packets from sensors to base stations along efficient routes. The algorithm eliminates the voids that cause non-optimal routing paths in geographic routing. It replaces the right-hand rule by distance upgrading. It is fully distributed and responds to topology changes instantly with localised operations. We formally prove the correctness of the algorithm and evaluate its performance by simulations.

Maximizing Lifetime for Data Aggregation in Wireless Sensor Networks

This paper studies energy efficient routing for data aggregation in wireless sensor networks. Our goal is to maximize the lifetime of the network, given the energy constraint on each sensor node. Using linear programming (LP) formulation, we model this problem as a multicommodity flow problem, where a commodity represents the data generated from a sensor node and delivered to a base station. A fast approximate algorithm is presented, which is able to compute (1-e)-approximation to the optimal lifetime for any e > 0. Then along this baseline, we further study several advanced topics. First, we design an algorithm, which utilizes the unique characteristic of data aggregation, and is proved to reduce the running time of the fastest existing algorithm by a factor of K, K being the number of commodities. Second, we extend our algorithm to accommodate the same problem in the setting of multiple base stations, and study its impact on network lifetime improvement. All algorithms are evaluated through both solid theoretical analysis and extensive simulation results.