Korea University of Technology and Education 2014 IEEE World Forum on Internet of Things(WF-IoT) Network Navigability in the Social Internet of Things Korea University of Technology and Education UoC Laboratory Raejin Jeong

1. Introduction Internet of Things(IoT) crucial point The number of objects connected to the network keeps increasing, leading to an enormous searching space. Network Traffic of centralized system In terms of the number of accesses to the devices, and of the number of queries received by the search engines. Scalability Issue Scalability issues will arise form the search of the right object that can provide the desired service.

1. Introduction Solution of Scalability Issus In the Social Internet of Things(SIoT), every node is an object capable of establishing social relationships with other things in an autonomous way according to rules set by the owner. Basic Idea of SIoT Network Every object can look for the desired service by using its relationship, querying its friends, the friends, of its friends and so on in a distributed manner. Benefit memory consumption computation power and battery efficacy of service search operation

1. Introduction The Selection of the Friendship this paper propose five heuristic which are based on local network properties and that are expected to have an impact on the overall network. analyzing the performance in terms of: giant components average degree of connection local clustering average path length

2. Background Social IoT model Parental Object Relationship (POR) : built in the same period by the same manufacturer Co-Location Object Relationship (CLOR) : based on the same location Co-Work Object Relationship (CWOR) : based on the same work Ownership Object Relationship (OOR) : owned by the same user Social Object Relationship (SOR) : object come into contact sporadically or continuously.

2. Background Service search in IoT Hierarchy Structure (Clustering) To cope with the large number of real-world entities by using a hierarchy of mediators [5, 12]. The case of scalability These approaches are not scalable in case of frequent data and network changes whereas work well in case of pseudo-static metadata.

2. Background 1 2 8 9 … … Service search in IoT Centralized system Step 1) Request matching Step 2) Response match obj 9 Step 3) interlinked with obj 9 search engine object Service search in IoT Centralized system A centralized system where objects are contacted based on a prediction model that calculates the probability of matching the query[4]. The case of scalability with network traffic The number of possible result is significantly larger than the number of actual results, so a lot of objects are contacted for no reason.

3. Reference scenario Distributed search The object asks its friends if they can provide a particular service or if they “know”. When node 1 need to particular service, It use its own friendship to look for. A node with the desired service, by contacting its friends and the friends of its friends.

3. Reference scenario Key aspect of Network Navigability Definition of Network Navigability A giant component must exist in the network, and the greatest distance between any pairs of nodes should not exceed log 2 (𝑁) , where 𝑁 is the number of node in the network[16, 17]. Example 1 Figure : Network is not navigable because there is no giant component, i. e. the network is not connected

3. Reference scenario Example 2 Example 3 The case of network traffic The nodes themselves have to communicate and exchange information among each other; either way a huge amount of traffic would be generated. Example 2 Figure : Now, there is a giant component, i. e. the network is connected However the greatest distance = 7, and 7 > log 2 (8) . Example 3 Figure : The network is navigable because there is a giant component and the greatest distance = 2, and 2 < log 2 9 =3.16 .

3. Reference scenario neighborhood degree local clustering coefficient Kleinberg conclude that there are path to choose form and only knows few information about its neighbors [18, 19]. 6 3 2 local clustering coefficient Watts and Strogatz is calculated for each node in a network. local clustering coefficient measures how close the neighbors of a node [20]. 𝐶 𝑙𝑜𝑐𝑎𝑙 𝑛 = 2∗ 𝑒 𝑛 𝑘 𝑛 ∗( 𝑘 𝑛 −1) 𝑘 𝑛 : the number of neighbors of node 𝑛. 𝑒 𝑛 : the number of edge among the neighors Figure : where node 1 wants to get access to the information owned by node 10.

3. Reference scenario c = 1 c = 1/3 c = 0 local clustering coefficient Watts and Strogatz is calculated for each node in a network. local clustering coefficient measures how close the neighbors of a node [20]. 𝐶 𝑙𝑜𝑐𝑎𝑙 𝑛 = 2∗ 𝑒 𝑛 𝑘 𝑛 ∗( 𝑘 𝑛 −1) c = 1 c = 1/3 𝑘 𝑛 : the number of neighbors of node 𝑛. 𝑒 𝑛 : the number of edge among the neighbors c = 0

4. Experimental Evaluation Selection of network links This paper propose five heuristics to help the nodes in the process of selection of the best set of friends. Example of 5 strategies 1. First Nmax connection : refuses any new request of friendship. >> refuse the request 2. Max neighborhood degree : accepts new friendship and discards the old node with low degree. >> node 4 is terminated 3. Min neighborhood degree : accepts new friendship and discards the old node with high degree. >>node 6 is terminated 4 2 3 5 Figure : where the maximum number of connection 𝑁 𝑚𝑎𝑥 = 5, node 2 send a friendship request to node 1.

Number of common friends 4. Experimental Evaluation Example of 5 strategies 4. A node accepts new friendships and discards the old ones in order to maximize its own local cluster coeffi-cient; the node sorts its friends by the number of their common friends and the node with the lowest value is discarded. >> node 3 is terminated 4 2 3 5 𝐶 𝑙𝑜𝑐𝑎𝑙 2 = 2∗2 4∗3 = 1 3 𝐶 𝑙𝑜𝑐𝑎𝑙 3 = 2∗0 3∗2 =0 𝐶 𝑙𝑜𝑐𝑎𝑙 4 = 2∗1 2∗1 =1 𝐶 𝑙𝑜𝑐𝑎𝑙 5 = 2∗3 4∗3 = 1 2 𝐶 𝑙𝑜𝑐𝑎𝑙 6 = 2∗1 5∗4 = 1 10 𝐶 𝑙𝑜𝑐𝑎𝑙 7 = 2∗1 4∗3 = 1 6 Figure : where the maximum number of connection 𝑁 𝑚𝑎𝑥 = 5, node 2 send a friendship request to node 1. Node2 Node3 Node4 Node5 Node6 Node7 Number of common friends 2EA (node 4, node 6) 0EA 1EA (node 5) (node 2, node 4) (node 2) 𝐶 𝑙𝑜𝑐𝑎𝑙 𝑛 = 2∗ 𝑒 𝑛 𝑘 𝑛 ∗( 𝑘 𝑛 −1) 𝑘 𝑛 : the number of neighbors of node 𝑛. 𝑒 𝑛 : the number of edge among the neighbors

Number of common friends 4. Experimental Evaluation Example of 5 strategies 5. Min Local Clustering : A node accepts new friendships and discards the old ones in order to minimize its own local cluster coeffi-cient; the node sorts its friends by the number of their common friends and the node with the highest value is discarded. >> node 5 is terminated 4 2 3 5 𝐶 𝑙𝑜𝑐𝑎𝑙 2 = 2∗2 4∗3 = 1 3 𝐶 𝑙𝑜𝑐𝑎𝑙 3 = 2∗0 3∗2 =0 𝐶 𝑙𝑜𝑐𝑎𝑙 4 = 2∗1 2∗1 =1 𝐶 𝑙𝑜𝑐𝑎𝑙 5 = 2∗3 4∗3 = 1 2 𝐶 𝑙𝑜𝑐𝑎𝑙 6 = 2∗1 5∗4 = 1 10 𝐶 𝑙𝑜𝑐𝑎𝑙 7 = 2∗1 4∗3 = 1 6 Figure : where the maximum number of connection 𝑁 𝑚𝑎𝑥 = 5, node 2 send a friendship request to node 1. 𝐶 𝑙𝑜𝑐𝑎𝑙 𝑛 = 2∗ 𝑒 𝑛 𝑘 𝑛 ∗( 𝑘 𝑛 −1) Node2 Node3 Node4 Node5 Node6 Node7 Number of common friends 2EA (node 4, node 6) 0EA 1EA (node 5) (node 2, node 4) (node 2) 𝑘 𝑛 : the number of neighbors of node 𝑛. 𝑒 𝑛 : the number of edge among the neighbors

4. Experimental Evaluation Simulation setup

4. Experimental Evaluation Simulation result

4. Experimental Evaluation Simulation result

5. Conclusion Propose Idea Future Works In this paper we have focused on the link selection in the social IoT. The driving idea is to select a narrow set of links in order for a node to manage more efficiently its friendships. Future Works we plan to focus on the service discovery in the same scenario and analyze the performance differences in finding the right object and service.