Fault tolerant aggregation in heterogeneous sensor networks

We present a protocol for secure data aggregation in wireless sensor networks that offers end-to-end data confidentiality by using homomorphic functions and interleaved encryption. Hop-by-hop aggregation in sensor networks is an efficient way to save energy. Node compromises in hostile environments require protocols for data aggregation where the intermediate nodes contribute with their own values to the aggregated data without getting access to it. Homomorphic encryption schemes allow aggregation on ciphertext and thus can provide end-to-end data confidentiality. We propose a layered data aggregation protocol which ensures that, in the presence of less than n captured nodes, an attacker cannot get access to any aggregated data from the network. When more than n nodes are captured, the attacker can only get access to the aggregated values received by the captured nodes. Our protocol is resilient to node failure and no pre-built tree for data aggregation is needed.

Minimizing latency is a major issue for data aggregation in wireless sensor networks (WSNs). Hence, the proposed algorithms must achieve the minimum delay in data delivery while decreasing the energy consumption. In this paper, we propose a new version of the prefix frequency filtering technique (PFF) proposed by [1], which aims to minimize aggregation latency. PFF finds similar sets of data generated by nodes in order to reduce redundancy in data over the network, thus, nodes consume less energy. While in the enhanced version of the PFF technique, called PPSFF, we propose a positional filtering that exploits the order of readings both in the prefix and the suffix of a set and leads to upper bound estimations of similarity scores. Experiments on real sensor data show that our enhancement can significantly improve the latency of the PFF technique without affecting its performance.

CORA: Correlation-based resilient aggregation in sensor networks

We consider distributed algorithms for data aggregation in sensor networks. The algorithms are admitted via message passing hence through pairwise computations. Under these algorithms a transmitting node becomes inactive until it is reactivated, yielding to substantial energy gains. We study the per-node message and time complexities of the algorithms in explicit graphs as a function of the network size. The obtained complexities are compared to those of so-called gossip algorithms. The results suggest trade-offs between the number of messages transmitted and the computation times.

Several sensor network applications, such as environmental monitoring, require data aggregation to an observer. For this purpose, a data aggregation tree, rooted at the observer, is constructed in the network. Node clustering can be employed to further balance load among sensor nodes and prolong the network lifetime. In this paper, we design and implement a system, iHEED, in which node clustering is integrated with multi-hop routing for TinyOS. We consider simple data aggregation operators, such as AVG or MAX. We use a simple energy consumption model to keep track of the battery consumption of cluster heads and regular nodes. We perform experiments on a sensor network testbed to quantify the advantages of integrating hierarchical routing with data aggregation. Our results indicate that the network lifetime is prolonged by a factor of 2 to 4, and successful transmissions are almost doubled. Clustering plays a dominant role in delaying the first node death, while aggregation plays a dominant role in delaying the last node death

We present geometric broadcast for sensor and actor networks (GBSA), a novel broadcasting protocol for heterogeneous wireless sensor and actor networks. While broadcasting is a very energy-expensive protocol, it is also widely used as a building block for a variety of other network layer protocols. Therefore, reducing the energy consumption by optimizing broadcasting is a major improvement in heterogenous sensor networking. GBSA is a distributed algorithm where nodes make local decisions on whether to transmit based on a geometric approach. GBSA does not need any neighborhood information and imposes very low communication overhead. GBSA is scalable to the change in network size, node type, node density and topology. GBSA accommodates seamlessly such network changes, including the presence of actors in heterogeneous sensor networks. Indeed GBSA takes advantage of actor nodes, and uses their resources when possible, thus reducing the energy consumption by sensor nodes. Through simulation evaluations, we show that GBSA is very scalable and its performance is improved by the presence of actors. At best of our knowledge, GBSA is the first broadcast protocol designed specifically for heterogeneous sensor and actor networks.

SummaryThe essential design concern of a sensor network is to balance energy consumption of sensor nodes (SNs) to prolong the network lifetime. Many research works cited that clustering techniques efficiently utilize the network's energy resource by organizing SNS into groups of clusters and benefits in reduced data transmission. An extensively used category of cluster‐based protocols is the probabilistic clustering technique in which a preset optimum likelihood is used to facilitate the selection procedure of cluster head (CH). These clustering techniques suffer from non‐uniform dissemination of CH, which leads to uneven load balance and uneven energy consumption of SNs during network activities like data transmitting and data receiving. This causes an energy‐hole problem and reduces network lifetime. In order to solve these issues, we have focused on to design a balanced cluster‐based data aggregation and formulate a method that increases the energy efficiency of probabilistic clustering techniques by optimizing the number of clusters and the dissemination of CHs in the sensor network. The simulation analysis proves that the proposed technique accomplishes significantly enhanced than the existing works.

In wireless sensor networks, data aggregation is critical to network lifetime. It implies that data will be aggregated while flowing from multiple sources to a specific node named sink. The construction of routing trees aware of the data aggregation has a considerable cost and solutions in the literature are not efficient for scenarios where the events are of short duration. This paper presents the Dynamic Data-Aggregation Aware Routing Protocol (DDAARP) for wireless sensor networks. This novel protocol builds dynamic routes, which improve the cost and quality of final routing tree. It also reduces the number of messages necessary to set up a routing tree, maximize the number of overlapping routes, selects routes with the highest aggregation rate, and performs reliable data aggregation transmission. DDAARP was compared with two existing solutions reported in the literature regarding communication costs, delivery efficiency and tree quality created. routing tree built by DDAARP has the best quality of aggregation compared with other algorithms. Results show that DDAARP outperforms these solutions for different scenarios in all evaluations performed. Furthermore, it also shows that the proposed algorithm is a good solution for scenarios with short-term events and for events of long duration.

We present a new sketch for summarizing network data. The sketch has the following properties which make it useful in communication-efficient aggregation in distributed streaming scenarios, such as sensor networks: the sketch is duplicate insensitive, i.e., reinsertions of the same data will not affect the sketch and hence the estimates of aggregates. Unlike previous duplicate-insensitive sketches for sensor data aggregation [S. Nath et al., Synposis diffusion for robust aggregation in sensor networks, in Proceedings of the 2nd International Conference on Embedded Network Sensor Systems, (2004), pp. 250–262], [J. Considine et al., Approximate aggregation techniques for sensor databases, in Proceedings of the 20th International Conference on Data Engineering (ICDE), 2004, pp. 449–460], it is also time decaying, so that the weight of a data item in the sketch can decrease with time according to a user-specified decay function. The sketch can give provably approximate guarantees for various aggregates of data, including the sum, median, quantiles, and frequent elements. The size of the sketch and the time taken to update it are both polylogarithmic in the size of the relevant data. Further, multiple sketches computed over distributed data can be combined without loss of accuracy. To our knowledge, this is the first sketch that combines all the above properties.

Wireless sensor networks (WSNs) is a class of wireless ad hoc networks in which sensor nodes sense, process, and communicate data acquired from the physical environment to an external-base-station (BS). But the fundamental challenge in the design of wireless sensor networks (WSNs) is to maximise their lifetimes especially when they have a limited and non-replenish able energy supply. Energy-efficient communication is necessary to increase the lifetime of the sensor network. In this work solutions are for the data gathering and routing problem with in network aggregation in WSNs. The aim of our work is to focus on data gathering and redundancy elimination with in-network aggregation in WSNs. The objective is to maximise the network lifetime by utilising data aggregation with redundancy removal by increasing accuracy thereby improving packet delivery ratio and reducing energy consumption. This work focuses on the maximum lifetime data gathering and routing to avoid packet loss in wireless sensor networks.

Wireless ad-hoc sensor networks have caught the fancy of many researchers through the world in a very small span of time. Although notable progress has been made in several key areas, several researchers falter in the way they look at sensor networks i.e. just another kind of wireless ad hoc networks, albeit one composed of energy constrained nodes. A key aspect that makes sensor networks different from traditional networks is their strong link to the physical world. We develop this perspective by studying an interesting class of sensor network applications called aggregation applications.We argue that when a user queries for aggregates like maximum, average etc., he is interested in knowing the statistics of the underlying physical process. In general, this is not equivalent to the statistics gathered from a bunch of nodes. We introduce a distinction between aggregates calculated over a region and aggregates calculated over a discrete set of nodes. We classify them as spatial and nodal aggregates respectively. Till now, researchers have followed the approach of doing nodal aggregation in response to every user query in sensor networks. For a continuous physical process and a random deployment of sensor nodes, which are a norm for sensor networks, the conventional approach of doing nodal aggregation produces inaccurate results.We verify our claim by studying a specific aggregation function namely the average over a region (spatial average) in detail. We use a voronoi-based approach to propose an algorithm for calculating spatial average in sensor networks. The algorithm can work in two modes -- centralized and distributed. The energy and accuracy tradeoff associated with each of the two modes is analyzed in detail. The efficacy of the proposed algorithm is tested over a wide variety of physical processes including real precipitation data of South America acquired over a span of past 50 years. We will show that our algorithm gives a 2-8 times performance gain as compared to the conventional approach of doing nodal aggregation. We further explore the robustness of our algorithm to link failures and channel impairments.Although not 100% accurate, our approach is a simple, efficient and a practical solution for calculating the spatial average. We have implemented our algorithm on Berkeley motes. Our first prototype implementation occupies less than 12K flash ROM and consumes less than 1.6K run-time memory. Our algorithm can be easily integrated with available frameworks for doing aggregation in sensor networks. For a minimal cost, our algorithm can be used to provide a spatial aggregation service on a network of motes.

Wireless sensor networks are now in widespread use to monitor regions, detect events and acquire information. To reduce the amount of sending data, an aggregation approach can be applied along the path from sensors to the sink. However, usually the carried information contains confidential data. Therefore, an end-to-end secure aggregation approach is required to ensure a healthy data reception. End-to-end encryption schemes that support operations over cypher-text have been proved important for private party sensor network implementations. Unfortunately, nowadays these methods are very complex and not suitable for sensor nodes having limited resources. In this paper, we propose a secure end-to-end encrypted-data aggregation scheme. It is based on elliptic curve cryptography that exploits a smaller key size. Additionally, it allows the use of higher number of operations on cypher-texts and prevents the distinction between two identical texts from their cryptograms. These properties permit to our approach to achieve higher security levels than existing cryptosystems in sensor networks. Our experiments show that our proposed secure aggregation method significantly reduces computation and communication overhead and can be practically implemented in on-the-shelf sensor platforms. By using homomorphic encryption on elliptic curves, we thus have realized an efficient and secure data aggregation in sensor networks.

Data Aggregation in Sensor Networks

Computing and maintaining network structures for efficient data aggregation incurs high overhead for dynamic events where the set of nodes sensing an event changes with time. Moreover, structured approaches are sensitive to the waiting time that is used by nodes to wait for packets from their children before forwarding the packet to the sink. Although structureless approaches can address these issues, the performance does not scale well with the network size. We propose tree on DAG (ToD), a semistructured approach that uses dynamic forwarding on an implicitly constructed structure composed of multiple shortest path trees to support network scalability. The key principle behind ToD is that adjacent nodes in a graph will have low stretch in one of these trees in ToD, thus resulting in early aggregation of packets. Based on simulations on a 2,000-node network and real experiments on a 105-node Mica2-based network, we conclude that efficient aggregation in large-scale networks can be achieved by our semistructured approach.

Wireless sensor networks have enormous potential to aid data collection in a number of areas, such as environmental and wildlife research. In this paper, we address the challenges of supporting many-to-many aggregation in a sensor network. An application of many-to-many aggregation is in-network control of sensors. For expensive sensing tasks such as sap flux measurements and camera repositioning, we use low-cost information obtained at multiple other nodes in the network to control such tasks, e.g., decreasing sampling rates when readings are predictable or unimportant, while increasing sampling rates when there are interesting activities. In general, there is a many-to-many relationship between sources (nodes providing control inputs) and destinations (nodes requiring control outputs). We present a method for implementing many-to-many aggregation in a sensor network that minimizes the communication cost by optimally balancing a combination of multicast and in-network aggregation. Our optimization technique is efficient in finding the initial solution and handling dynamic updates.