Distributed Computing in Sensor Systems: Third IEEE by Faisal Qureshi, Demetri Terzopoulos (auth.), James Aspnes,

By Faisal Qureshi, Demetri Terzopoulos (auth.), James Aspnes, Christian Scheideler, Anish Arora, Samuel Madden (eds.)

The ebook constitutes the refereed court cases of the 3rd foreign convention on dispensed Computing in Sensor structures, DCOSS 2007, held in Sante Fe, NM, united states in June 2007.

The 27 revised complete papers offered have been rigorously reviewed and chosen from seventy one submissions. The papers classification in 3 tracks protecting the components of algorithms, purposes, and platforms, therefore bridging the distance among idea and perform and among the wider box of dispensed computing and the explicit concerns coming up in sensor networks and similar systems.

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Extra info for Distributed Computing in Sensor Systems: Third IEEE International Conference, DCOSS 2007, Santa Fe, NM, USA, June 18-20, 2007. Proceedings

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M − i)! 2 2 Figure 2 shows the false suppression rate under different settings of l, m, and M . We can clearly see that the false suppression rate is very low for a reasonable setting of l, m and M . Overheads: Compared with the baseline approach, the additional storage overhead at sensor nodes mainly comes from the public keys of the trusted server and the system users. The additional computation overhead at sensor nodes mainly comes from the signature verification. Since the DoS attacks against signature verification can be greatly reduced by our weak authentication method, we believe that supporting broadcast query using ECC-based signature schemes is practical for the current generation of sensor networks.

Attack Model: An attacker can launch a wide range of attacks against the network. For example, he can simply perform a denial-of-service (DoS) attack to jam the wireless channel and disable the network operation. Such DoS attack is easy to mount and common to the protocols in every sensor network. There are no effective ways to stop the attacker from doing such attack. However, in this paper, we are more interested in the attacks whose goal is to access the valuable sensor data that he is not supposed to access.

The user’s capability string can be also viewed as a constraint. A constraint can be built from multiple constraints by simply using “AND” (∧) or “OR” (∨) operator. For example, “(A(d) = humidity ∧ O = 01) ∧ (R ≥ 30)” 24 D. Liu means that the user can only read the sensed humidity at a rate of no more than one packet per 30 seconds. In this constraint, the first part is the capability string of the user, while the second part (R ≥ 30) is an additional constraint on the packet rate. A constraint can usually be written in an appropriate formal language.

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