Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userEfficient Data Gathering and Aggregation for Multiple Applications in Wireless Sensor Networks
166
0
0.0
(
0
)
Added by
Fathima Begum M
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Data aggregation in wireless sensor networks refers to acquiring the sensed data from the sensors to the gateway node. It reduces the amount of power consumed during data transmission between the sensor nodes. Generally homomorphic encryptions have been applied to conceal communication during aggregation. Since enciphered data can be aggregated algebraically without decryption. Here adversaries are able to forge aggregated results by compromising them. However, these schemes are not satisfying multi-application environments, provide insecure transmission and do not provide secure counting for unauthorized aggregation attacks. In this paper, we propose a new concealed data aggregation scheme extended from homomorphic privacy encryption system. The proposed scheme designed for a multi-application environment, mitigates the impact of compromising attacks in single application environments and also it can avoid the damage from unauthorized aggregations by the privacy homomorphic encryption scheme.
rate research
Read More
Wireless Sensor Networks (WSNs) rely on in-network aggregation for efficiency, however, this comes at a price: A single adversary can severely influence the outcome by contributing an arbitrary partial aggregate value. Secure in-network aggregation can detect such manipulation. But as long as such faults persist, no aggregation result can be obtained. In contrast, the collection of individual sensor node values is robust and solves the problem of availability, yet in an inefficient way. Our work seeks to bridge this gap in secure data collection: We propose a system that enhances availability with an efficiency close to that of in-network aggregation. To achieve this, our scheme relies on costly operations to localize and exclude nodes that manipulate the aggregation, but emph{only} when a failure is detected. The detection of aggregation disruptions and the removal of faulty nodes provides robustness. At the same time, after removing faulty nodes, the WSN can enjoy low cost (secure) aggregation. Thus, the high exclusion cost is amortized, and efficiency increases.
Recently, many researchers have studied efficiently gathering data in wireless sensor networks to minimize the total energy consumption when a fixed number of data are allowed to be aggregated into one packet. However, minimizing the total energy consumption does not imply the network lifetime is maximized. In this paper, we study the problem of scheduling data aggregation trees working for different time periods to maximize the network lifetime when a fixed number of data are allowed to be aggregated into one packet. In addition, we propose a heuristic to balance the lifetime of nodes in data aggregation trees such that the network lifetime is maximized. Simulation results show that the proposed heuristic provides a good performance.
In wireless sensor networks (WSNs), the Eschenauer-Gligor (EG) key pre-distribution scheme is a widely recognized way to secure communications. Although connectivity properties of secure WSNs with the EG scheme have been extensively investigated, few results address physical transmission constraints. These constraints reflect real-world implementations of WSNs in which two sensors have to be within a certain distance from each other to communicate. In this paper, we present zero-one laws for connectivity in WSNs employing the EG scheme under transmission constraints. These laws help specify the critical transmission ranges for connectivity. Our analytical findings are confirmed via numerical experiments. In addition to secure WSNs, our theoretical results are also applied to frequency hopping in wireless networks.
We propose an algorithm which produces a randomized strategy reaching optimal data propagation in wireless sensor networks (WSN).In [6] and [8], an energy balanced solution is sought using an approximation algorithm. Our algorithm improves by (a) when an energy-balanced solution does not exist, it still finds an optimal solution (whereas previous algorithms did not consider this case and provide no useful solution) (b) instead of being an approximation algorithm, it finds the exact solution in one pass. We also provide a rigorous proof of the optimality of our solution.
Resource and cost constraints remain a challenge for wireless sensor network security. In this paper, we propose a new approach to protect confidentiality against a parasitic adversary, which seeks to exploit sensor networks by obtaining measurements in an unauthorized way. Our low-complexity solution, GossiCrypt, leverages on the large scale of sensor networks to protect confidentiality efficiently and effectively. GossiCrypt protects data by symmetric key encryption at their source nodes and re-encryption at a randomly chosen subset of nodes en route to the sink. Furthermore, it employs key refreshing to mitigate the physical compromise of cryptographic keys. We validate GossiCrypt analytically and with simulations, showing it protects data confidentiality with probability almost one. Moreover, compared with a system that uses public-key data encryption, the energy consumption of GossiCrypt is one to three orders of magnitude lower.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
