Distributing codes to specific target sensors in order to fix bugs and/or install a new application is an important management task in WSNs (Wireless Sensor Networks). For the energy efficient dissemination of such codes to specific target sensors, it is required to select the minimum required number of forwarders with the fewest control messages. In this paper, we propose a novel RPL (Routing Protocol for Low-power and lossy networks)-based tree construction scheme for target-specific code dissemination, which is called R-TCS. The main idea of R-TCS is that by leveraging the data collection tree created by a standard routing protocol RPL, it is possible to construct the code dissemination tree with the minimum numbers of non-target sensors and control messages. Since by creating a data collection tree each sensor exchanges RPL messages with the root of the tree, every sensor knows which sensors compose its upwards route, i.e. the route towards the root, and downwards route, i.e. the route towards the leaves. Because of these properties, a target sensor can select the upward route that contains the minimum number of non-target sensors. In addition, a sensor whose downward routes do not contain a target sensor is not required to transmit redundant control messages which are related to the code dissemination operation. In this way, R-TCS can reduce the energy consumption which typically happens in other target-specific code dissemination schemes by the transmission of control messages. In fact, various performance evaluation results obtained by means of computer simulations show that R-TCS reduces by at least 50% energy consumption as compared to the other previous known target-specific code dissemination scheme under the condition where ratio of target sensors is 10% of all sensors.

In RFID-enabled supply chains, it is necessary to protect the contents of EPCs (Electronic Product Code) since an EPC contains sensitive information such as the product code and serial number and could be used for counterfeits. Although many protection schemes have been proposed, no scheme can limit the number of illegal attempts for discovering EPCs or notice whether an attacker exists. In this paper, we propose an illegal interrogation detectable products distribution scheme for RFID-enabled supply chains. The idea is to detect the attacker by forcing him/her to access an authentication server. Our scheme masks EPCs with random sequences. Masked EPCs are written into genuine tags on products while random sequences are placed on an authentication server with an access code. An access code is divided into shares with a secret sharing scheme and they are written into genuine tags. We also write bogus shares into extra off-the-shelf tags that are not attached to any products. Since an attacker who wants to know genuine EPCs may obtain a large number of access code candidates and must try each on the authentication server, the server can detect the attacker.

On SNS (Social Networking Services), detecting Sybils is an urgent demand. The most famous approach is called “SybilRank” scheme where each node evenly distributes its trust value starting from honest seeds and detects Sybils based on the trust value. Furthermore, Zhang et al. propose to avoid trust values from being distributed into Sybils by pruning suspicious relationships before performing SybilRank. However, we point out that the above two schemes have shortcomings that must be remedied. In the former scheme, seeds are concentrated on the specific communities because they are selected from nodes that have largest number of friends, and thus the trust value is not evenly distributed. In the latter one, a sophisticated attacker can avoid graph pruning by making relationships between Sybil nodes. In this paper, we propose a robust seed selection and graph pruning scheme to detect Sybil nodes more accurately. To more evenly distribute trust value into honest nodes, we first detect communities in the SNS and select honest seeds from each detected community. And then, by leveraging the fact that Sybils cannot make dense relationships with honest nodes, we also propose a graph pruning scheme based on the density of relationships between trusted nodes. We prune the relationships which have sparse relationships with trusted nodes and this enables robust pruning malicious relationships even if the attackers make a large number of common friends. By the computer simulation with real dataset, we show that our scheme improves the detection accuracy of both Sybil and honest nodes.