# SIoT: A New Strategy to Improve the Network Lifetime with an Efficient Search Process
## 2 Related Works  
The social networks' navigability improvement is equivalent to the achievement of the following criteria, stated simultaneously: maximizing the giant component and local clustering coefficient, and minimizing the average degree, the average path length and the diameter. In [24], the authors presented five strategies to select the friendships in a network. At first, a node accepts all the friendship requests until it reaches the maximum number of allowed connections. Then, to manage any further requests, one of the following strategies is proposed to select the new friendships:  
* **Strategy 1:** A node accepts any new request of friendship. * **Strategy 2:** A node accepts requests of nodes that have a greater number of connections (decreasing order). * **Strategy 3:** A node accepts requests of nodes that have a fewer number of connections (increasing order). * **Strategy 4:** A node accepts requests of nodes that have a greater number of common connections. * **Strategy 5:** A node accepts requests of nodes that have a fewer number of common connections. These five strategies are applied separately to create the social network. The results show that strategies 3 and 5 achieve the total giant component of nodes, strategies 2 and 5 have a low average path length, strategies 2 and 4 have the lowest average degrees, and strategies 1 and 4 have a high value of local clustering coefficient. The algorithm in [25] based on the strategies presented in [24] applies the genetic algorithm for friendship selection in IoT. The algorithm is used to maximize the number of friends (strategy 2) and friends of friends (common friends) (strategy 4) simultaneously, for selecting the optimal near node i.e., reaching the destinations rapidly. The results achieve an enhancement in the shortest path length, and also an acceptable average degree and average local clustering coefficient values in comparison with the work presented in [24]. A heuristic method is proposed in [26] to select friends in the SIoT, based on reachability and type of relationship properties. When a node achieves the maximal number of friends and there is another node that sends a friend request, it checks the ability of its friends and the candidate friend to achieve their destinations, by calculating the reachability factor and verifying the relationship type. Based on these two factors, the node decides to add or to reject the candidate friend. This heuristic method improves the average degree and average path length, but the giant component is modest (52%). Authors in [27] proposed an algorithm to resolve the link selection (friends selection) issue in SIoT, by restricting the number of connections for each node using a threshold value in order to improve the properties of navigability: path length, average clustering coefficient, and giant component. The threshold value is adjusted dynamically based on the number of hubs in network, which allows to add new friends and to remove old mutual friends. The experimental results show higher average clustering coefficient, shorten path length, and higher giant component. The enhancement of the properties of navigability makes service discovery task faster. To decrease the path length from a requestor node to a provider node, authors in [28] proposed two mechanisms:  
* **A caching system**: The nodes store pieces of information such as a document, web page or video. The storage location is based on the requestors of these services: the more these services are requested, the closer they are to the node's storage location. * **Friendship selection:** Established when a node creates new paths of friendship to decrease the path length to the provider node and removes friends that do not belong to the shortest path. The results show that the caching system strategy has a less than average hop length than the friendship selection strategy but it is expensive in terms of memory consumption.