# CATSWoTS: Context Aware Trustworthy Social Web of Things System
## 4 Experimental Validation and Results Discussion  
This section presents the efficacy of our proposed system. To evaluate the performance and prove the effectiveness of CATSWoTS, the validation was done in a specifically designed environment. For experimental setup, the following roles were developed:  
1. A central server where a context manager and a QoS manager resides. An SP may register the services there. 2. The nodes that may play the role of service providers and service consumers as well. These nodes are also connected through a peer to peer network. Each node has two local managers for context, QoS and trust management. It has its own local database on which the profiles of consumed services are maintained. The trust-based invocation is generated based on real dataset [38]. The feedback about the QoS of 2507 services were used in this experiment. At the server level, the profile of 2507 services resides, whereas each node contains a dataset of 50-100 profile of each consumed service. The nodes can communicate with each other and with the central server as well. The available dataset evaluated the services with respect to QoS constraints only; however, the context related attributes are added by considering the general criteria so that it can be applied to any kind of service for IoT and to evaluate  
Figure 5: Flow chart summarizing the whole process. the effectiveness of CATSWoTS. Therefore, the following attributes have been added to accommodate the context awareness specifically related to IoT services. 1. Capacity: A set of attributes defining the capacity of any service may include battery power, processing speed, memory, and another service-related capability. For example, [PERSON] wants the garbage collection services of IoT based waste management company. In this case, the attributes may include smart bin's capacity to contain garbage in kilograms, battery, processing power and memory of sensors and actuators that constantly monitor the level of garbage in the bin and then intimate the SP when it needs to empty the bin. However, to keep the attribute simple and generic for testing purposes, three levels of capacity are defined which are (i) low, (ii) medium (iii) and high. 2. Location: Location, where the services are provided, is described in terms of longitude and latitude. 3. Time: the timings of services are described as (i) morning, (ii) noon (iii) evening and (iv) 24 h like the waste management company may provide the services in the morning but [PERSON] wants the services in the noon then [PERSON] is in the search of an SP which offers the services in noon. A social IoT environment with \(n=30\) nodes is created. These nodes form a social circle with each other. Thirty nodes are further divided into three subgroups to send multiple requests from different social groups. In Equations (1)-(3), three lists are being maintained. Some nodes are directly connected with each other, whereas others are indirectly connected through their circle with each other. As described previously, each node is assumed to consume 50-100 services and hence maintains the feedback of these services, which is considered as direct trust. Any service seeking node requests the recommendation of a particular service and, upon consumption of service, direct trust assessment will be updated, whereas indirect assessment from where the recommendation comes will be updated using Equations (4) and (5). Trust value ranges from 0 to 1 where 0 means not satisfied, 0.5 means satisfied and 1 means highly satisfied. Initially, \(T_{j}\) in (6) is set to 0.5. Gradually, trust is built gradually when nodes interact with each other by seeking and consuming services after getting feedback. The proposed concept is evaluated based on its core ideas which are context awareness and QoS constraints-based trust management. It is done by experimenting at SS and SP nodes and then identifying the potential services based on the prescribed criteria.