Optimal processing node discovery algorithm for distributed computing in IoT

© 2015 IEEE.The number of Internet-connected sensing and control devices is growing. Some anticipate them to number in excess of 212 billion by 2020. Inherently, these devices generate continuous data streams, many of which need to be stored and processed. Traditional approaches, whereby all data are shipped to the cloud, may not continue to be effective as cloud infrastructure may not be able to handle myriads of data streams and their associated storage and processing needs. Using cloud infrastructure alone for data processing significantly increases latency, and contributes to unnecessary energy inefficiencies, including potentially unnecessary data transmission in constrained wireless networks, and on cloud computing facilities increasingly known to be significant consumers of energy. In this paper we present a distributed platform for wireless sensor networks which allows computation to be shifted from the cloud into the network. This reduces the traffic in the sensor network, intermediate networks, and cloud infrastructure. The platform is fully distributed, allowing every node in a homogeneous network to accept continuous queries from a user, find all nodes satisfying the users query, find an optimal node (Fermat-Weber point) in the network upon which to process the query, and provide the result to the user. Our results show that the number of required messages can be decreased up to 49% and processing latency by 42% in comparison with state-of-the-art approaches, including Innet

From Task Graphs to Concrete Actions: A New Task Mapping Algorithm for the Future Internet of Things

International audienceTask mapping, which basically consists of mapping a set of tasks onto a set of nodes, is a well-known problem in distributed computing research. As a particular case of distributed systems, the Internet of Things (IoT) poses a set of renewed challenges, because of its scale, heterogeneity and properties traditionally associated with wireless sensor networks (WSN), shared sensing, continous processing and real time computing. To handle IoT features, we present a formalization of the task mapping problem that captures the varying consumption of resources and various constraints (location, capabilities, QoS) in order to compute a mapping that guarantees the lifetime of the concurrent tasks inside the network and the fair allocation of tasks among the nodes. It results in a binary programming problem for which we provide an efficient heuristic that allows its resolution in polynomial time. Our experiments show that our heuristic: (i) gives solutions that are close to optimal and (ii) can be implemented on reasonably powerful Things and performed directly within the network, without requiring any centralized infrastructure

A novel distributed framework for optimizing query routing trees in wireless sensor networks via optimal operator placement

In this paper, we focus the attention on the operator placement problem in Wireless Sensor Networks (WSN). This problem is very relevant for in-network query processing over WSN, where query routing trees are decomposed into three sub-components that must be processed at query time, namely operator tree, operator placement assignment scheme and routing scheme. In particular, the operator placement assignment defines on which node of the network each (query) operator will be hosted and executed. Hence, operator placement plays a key role in the context of query optimization issues in WSN research. In line with this main motivation, in this paper we present an optimal distributed algorithm to adapt the placement of a single operator in high communication cost networks, such as a wireless sensor network. Our parameter-free algorithm finds the optimal node to host the operator with minimum communication cost overhead. Three techniques, proposed here, make this feature possible: (1) identifying the special, and most frequent case, where no flooding is needed, otherwise (2) limitation of the neighborhood to be flooded and (3) variable speed flooding and eves-dropping. When no flooding is needed the communication cost overhead for adapting the operator placement is negligible. In addition, our algorithm does not require any extra communication cost while the query is executed. In our experiments we show that for the rest of cases our algorithm saves 30%-85% of the energy compared to previously proposed techniques. To our knowledge this is the first optimal and distributed algorithm to solve the 1-median (Fermat node) problem. A comprehensive experimental evaluation and the proposal of two solutions that are capable of dealing with adaptive properties of the operator placement problem, which is an innovative perspective of research in this scientific field, represent two further contributions of our research. © 2012 Published by Elsevier Inc

A Novel Distributed Framework For Optimizing Query Routing Trees In Wireless Sensor Networks Via Optimal Operator Placement

In this paper, we focus the attention on the operator placement problem in Wireless Sensor Networks (WSN). This problem is very relevant for in-network query processing over WSN, where query routing trees are decomposed into three sub-components that must be processed at query time, namely operator tree, operator placement assignment scheme and routing scheme. In particular, the operator placement assignment defines on which node of the network each (query) operator will be hosted and executed. Hence, operator placement plays a key role in the context of query optimization issues in WSN research. In line with this main motivation, in this paper we present an optimal distributed algorithm to adapt the placement of a single operator in high communication cost networks, such as a wireless sensor network. Our parameter-free algorithm finds the optimal node to host the operator with minimum communication cost overhead. Three techniques, proposed here, make this feature possible: (1) identifying the special, and most frequent case, where no flooding is needed, otherwise (2) limitation of the neighborhood to be flooded and (3) variable speed flooding and eves-dropping. When no flooding is needed the communication cost overhead for adapting the operator placement is negligible. In addition, our algorithm does not require any extra communication cost while the query is executed. In our experiments we show that for the rest of cases our algorithm saves 30%\u201385% of the energy compared to previously proposed techniques. To our knowledge this is the first optimal and distributed algorithm to solve the 1-median (Fermat node) problem. A comprehensive experimental evaluation and the proposal of two solutions that are capable of dealing with adaptive properties of the operator placement problem, which is an innovative perspective of research in this scientific field, represent two further contributions of our research

Hybridization of enhanced ant colony system and Tabu search algorithm for packet routing in wireless sensor network

In Wireless Sensor Network (WSN), high transmission time occurs when search agent focuses on the same sensor nodes, while local optima problem happens when agent gets trapped in a blind alley during searching. Swarm intelligence algorithms have been applied in solving these problems including the Ant Colony System (ACS) which is one of the ant colony optimization variants. However, ACS suffers from local optima and stagnation problems in medium and large sized environments due to an ineffective exploration mechanism. This research proposes a hybridization of Enhanced ACS and Tabu Search (EACS(TS)) algorithm for packet routing in WSN. The EACS(TS) selects sensor nodes with high pheromone values which are calculated based on the residual energy and current pheromone value of each sensor node. Local optima is prevented by marking the node that has no potential neighbour node as a Tabu node and storing it in the Tabu list. Local pheromone update is performed to encourage exploration to other potential sensor nodes while global pheromone update is applied to encourage the exploitation of optimal sensor nodes. Experiments were performed in a simulated WSN environment supported by a Routing Modelling Application Simulation Environment (RMASE) framework to evaluate the performance of EACS(TS). A total of 6 datasets were deployed to evaluate the effectiveness of the proposed algorithm. Results showed that EACS(TS) outperformed in terms of success rate, packet loss, latency, and energy efficiency when compared with single swarm intelligence routing algorithms which are Energy-Efficient Ant-Based Routing (EEABR), BeeSensor and Termite-hill. Better performances were also achieved for success rate, throughput, and latency when compared to other hybrid routing algorithms such as Fish Swarm Ant Colony Optimization (FSACO), Cuckoo Search-based Clustering Algorithm (ICSCA), and BeeSensor-C. The outcome of this research contributes an optimized routing algorithm for WSN. This will lead to a better quality of service and minimum energy utilization