## 1 Introduction  
Advances in wireless communications and integrated circuits have enabled the development of small, smart and inexpensive wireless sensor devices. We envision that in the very near future these sensor nodes will be embedded into a multitude of human-carried devices. A large number of these mobile devices can be dynamically networked together and form wireless sensor networks (WSNs). Due to unpredictable human mobility, topology changes, limited transmission range of sensor nodesand so on, this type of network often exhibits extremely low and intermittent connectivity. Intermittent connectivity is a key characteristic of delay tolerant mobile sensor networks (DTMSNs) [1,2] while the network formed by mobile devices is one popular communication paradigm of DTMSNs. Data gathering is one of the major functions of WSNs. Data gathering approaches in the traditional WSNs usually rely on a large number of densely deployed sensor nodes with short range radio to form a well connected end-to-end network. Sensors in the network collaborate with each other to collect the target data information and transmit them to the sink nodes [3]. Such approaches, however, may not work effectively in DTMSNs due to intermittent network connectivity and sensor node mobility. Thus, routing in DTMSNs is a quite challenging problem. Meanwhile, currently, most DTMSN routing schemes just use simplistic random models for algorithm design and performance evaluation. e.g., the most commonly used mobility model is random waypoint [4] in which all nodes move randomly and their moving speed and direction are drawn from the same distributions. However, due to the differences in human behavior, the random waypoint model cannot reflect realistic human mobility characteristics [5]. Therefore, previous DTMSN routing protocols would not work well in mobile human applications and new routing schemes are required. There have been a number of routing techniques proposed for human-carried scenarios in disconnected delay tolerant networks. In [6], the authors imagine that each node in the network has a label telling others about its affiliation. A node only chooses to forward messages to destinations, or to next-hop nodes belonging to the same group (same label) as the destinations. It was demonstrated that LABEL significantly improves forwarding efficiency over oblivious forwarding using their one dataset, but it lacks a mechanism to move messages away from the source when the destinations are socially far away. [PERSON] _et al._[7] presented an analytical foundation on the impact of human mobility on the design of opportunistic forwarding algorithms based on six real human mobility traces from four different research groups. They established the inter-contact intervals, and contact durations for a wide range of typical human mobility patterns and for a variety of radio devices. Critically, it was shown that stateless forwarding schemes would not provide a bounded expected mean delivery latency across such systems. In this paper, we present an agenda based routing protocol, ARP, which uses peoples' agendas and their social relationships to carry out routing. Since people have varying social roles in the real world, some people may more popular and interact with sink nodes more often than others in the network. Thus in ARP, humans are ranked based on their popularity and routing decisions are made according to two key variables, which are transmission rankings and communication probabilities of sensor nodes, respectively. The first part is to route messages to nodes whose transmission rankings are higher than the current node, while the second part is to identify node's encounter time with sink nodes and to use them as relays. Moreover, ARP also employs the message survival time to decide a message's transmission or dropping to minimize transmission overhead.