Agent-based web for information fusion in military intelligence, surveillance, and reconnaissance

Leuchter, Sandro; Mühlenberg, Dirk; Schönbein, Rainer

doi:10.1109/icsmc.2008.5054671

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…Wireless sensor networks (WSNs), which can have hundreds or thousands of sensor nodes deployed over a monitored region, are being increasingly used in several applications such as military reconnaissance, target tracking, environmental monitoring, and medical monitoring [ 1 , 2 , 3 , 4 ]. In these large-scale wireless sensor networks (LWSNs), the amount of data processed is very large and the data redundancy is very high, but the sensing devices (nodes) in LWSNs have limited computing power and communication resources, leading to network performance degradation or collapse once the nodes’ limited power is depleted [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks

Chen

et al. 2015

Sensors

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With the rapid development of wireless communication technology, sensor technology, information acquisition and processing technology, sensor networks will finally have a deep influence on all aspects of people’s lives. The battery resources of sensor nodes should be managed efficiently in order to prolong network lifetime in large-scale wireless sensor networks (LWSNs). Data aggregation represents an important method to remove redundancy as well as unnecessary data transmission and hence cut down the energy used in communication. As sensor nodes are deployed in hostile environments, the security of the sensitive information such as confidentiality and integrity should be considered. This paper proposes Fully homomorphic Encryption based Secure data Aggregation (FESA) in LWSNs which can protect end-to-end data confidentiality and support arbitrary aggregation operations over encrypted data. In addition, by utilizing message authentication codes (MACs), this scheme can also verify data integrity during data aggregation and forwarding processes so that false data can be detected as early as possible. Although the FHE increase the computation overhead due to its large public key size, simulation results show that it is implementable in LWSNs and performs well. Compared with other protocols, the transmitted data and network overhead are reduced in our scheme.

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Section: Introductionmentioning

confidence: 99%

Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks

Chen

et al. 2015

Sensors

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Pragmatic Interoperability: A Systematic Review of Published Definitions

Asuncion

Sinderen

2010

IFIP Advances in Information and Communication Technology

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Abstract. Today's researchers argue that facilitating interoperability between applications by relying on agreements concerning the format and meaning (i.e. syntax and semantics) of exchanged data and the ordering of these exchanges are not enough to achieve a complete, effective and meaningful collaboration. The use of data (i.e. pragmatics) is important as well. Pragmatic interoperability thus requires that the use of data be mutually understood between collaborating systems. However, we observe that the notion of pragmatic interoperability is still largely unsettled, as evidenced by the various proposed definitions and the lack of a canonical understanding. Therefore, our objective is to contribute to a more in-depth understanding of this concept through a systematic review of published definitions. Our results show that, indeed, various interpretations of pragmatic interoperability exist. If we categorize the concepts that can be derived from these definitions, we see two broad categories: system level and business level. Within the scope of each of these levels, we see some degree of agreement among the definitions. However, comparing the definitions across these levels, we observe no general agreement. At the system level, a shared understanding of the intended and actual use of exchanged system message in a given context is essential. At the business level, pragmatic interoperability goes beyond service use by considering also the compatibility of business intentions, business rules, organizational policies, and the establishment and maintenance of trust and reputation mechanisms between collaborating business parties.

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Adversarial Deep Reinforcement Learning Enabled Threat Analytics Framework for Constrained Spatio-Temporal Movement Intelligence Data

Ghadermazi¹,

Hore²,

Sharma³

et al. 2021

2021 IEEE International Conference on Intelligence and Security Informatics (ISI)

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Agent-based web for information fusion in military intelligence, surveillance, and reconnaissance

Cited by 3 publications

References 3 publications

Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks

Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks

Pragmatic Interoperability: A Systematic Review of Published Definitions

Adversarial Deep Reinforcement Learning Enabled Threat Analytics Framework for Constrained Spatio-Temporal Movement Intelligence Data

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